Reinforcement learning algorithms that use policy gradient methods approach an optimal policy faster than Q-learning but at the cost of incurring high variances in gradients . Among variance reduction techniques are actor-critic methods that use value and advantage functions to train a policy actor . Policy Gradient methods that explore directly in parameter space are among the most effective and robust direct policy search methods and have drawn a lot of attention lately . The basic method from this field , Policy Gradients with Parameter-based Exploration , uses two samples that are symmetric around the current hypothesis to circumvent misleading reward in asymmetrical reward distributed problems gathered with the usual baseline approach . A number of adversarial learning methods have been proposed to learn the reward function together with the dialog policy . However , to alternatively update the dialog policy and the reward model on the fly , the algorithms to update the dialog policy are limited to policy gradient-based algorithms , such as REINFORCE and PPO . Besides , the alternative training of the dialog agent and the reward model can easily get stuck in local optimum or result in mode collapse . This paper sets forth a framework for deep reinforcement learning as applied to market making ( DRLMM ) for cryptocurrencies . Two advanced policy gradient-based algorithms were selected as agents to interact with an environment that represents the observation space through limit order book data , and order flow arrival statistics . Within the experiment , a forward-feed neural network is used as the function approximator and two reward functions are compared . We show that usual likelihood ratio methods used in discrete-time , fail to proceed the gradient because they are subject to variance explosion when the discretization time-step decreases to 0 . We describe an alternative approach based on the approximation of the pathwise derivative , which leads to a policy gradient estimate that converges almost surely to the true gradient when the time-step tends to 0 . The underlying idea starts with the derivation of an explicit representation of the policy gradient using pathwise derivation . Under this framework , the equivalence of timeout and stochastic policies on power-performance tradeoff is revealed , and the equivalent relation between these two types of DPM policy is derived . Then , a reinforcement learning algorithm that combines policy gradient estimate and stochastic approximation is proposed for optimising timeout policy online . This algorithm does not depend on any prior knowledge of system parameters , and can achieve a global optimum with less computational cost . However , this strategy has numerous problems , including variations between the populations being studied , different methods of data collection , and secular trends . Two analyses that are useful for policy changes and do not rely on external control groups are interrupted time-series analysis and policy gradient analysis . Interrupted Time-Series Analysis A common design used for evaluating policy change compares study outcomes during a specified period preceding the change with outcomes during a similar period after the change . With the soft gradient policy theorem , we construct the maximum entropy reinforcement learning framework for on-policy RL . For policy gradient based on-policy algorithms , policy network is often represented as Gaussian distribution with the action variance restricted to be global for all the states observed from the environment . We propose an idea called action variance scale for policy network and find it can work collaboratively with the idea of entropy regularization . BLRL starts with an initial set of parameterized boundaries that partition the state space into distinct control modes . Reinforcement reward is used to update the boundary parameters using the policy gradient formulation of Sutton et al. ( 2000 ) . We demonstrate that stochastic search can be limited to regions near mode boundaries , thus greatly reducing search , while still guaranteeing convergence to a locally optimal deterministic mode switching policy . Instead of directly maximizing rewards , GASIL focuses on reproducing past good trajectories , which can potentially make long-term credit assignment easier when rewards are sparse and delayed . GASIL can be easily combined with any policy gradient objective by using GASIL as a learned shaped reward function . Our experimental results show that GASIL improves the performance of proximal policy optimization on 2D Point Mass and MuJoCo environments with delayed reward and stochastic dynamics . This paper presents PolicyGAN , a policy gradient paradigm for training Generative Adversarial Networks that views the generator as an image generation neural agent which is rewarded by another neural agent , termed as the discriminator . Rewards are higher for samples near the original data manifold . We study a model of neuronal specialization using a policy gradient reinforcement approach . ( 1 ) The neurons stochastically fire according to their synaptic input plus a noise term ; ( 2 ) The environment is a closed-loop system composed of a rotating eye and a visual punctual target ; ( 3 ) The network is composed of a foveated retina , a primary layer and a motoneuron layer ; ( 4 ) The reward depends on the distance between the subjective target position and the fovea and ( 5 ) the weight update depends on a Hebbian trace defined according to a policy gradient principle . In order to take into account the mismatch between neuronal and environmental integration times , we distort the firing probability with a " pink noise " term whose autocorrelation is of the order of 100 ms , so that the firing probability is overestimated ( or underestimated ) for about 100 ms periods . ( iii ) The gradients of the Lagrangian associated with the safe-learning problem can be easily computed using standard policy gradient results and stochastic approximation tools . Leveraging these advantages , we establish that primal-dual algorithms are able to find policies that are safe and optimal . However the framework is also suitable for discrete units , and the only modification needed is to replace the Gaussian with a multinomial parameterised by the outputs of a softmax function . Though the reparameterisation trick for continuous variables is not applicable for this case , a policy gradient approach mnih2014neural can help to alleviate the high variance problem during stochastic estimation . proposed a variational inference framework for semi - supervised learning , but the prior distribution over the hidden variable remains as the standard Gaussian prior , while we apply a conditional parameterised Gaussian distribution , which is jointly learned with the variational distribution . We now discuss how to train the model parameters ( including and ) from a training dataset using reinforcement learning . One approach is the policy gradient method ( REINFORCE ) , which uses the current policy to roll out multiple trajectories to estimate a stochastic gradient , and then updates the policy via stochastic gradient ascent . Previous RL - based KBC methods typically use REINFORCE to learn the policy . Notice that the above procedure is fully differentiable . One can train G θ in an end - to - end manner using a policy gradient algorithm such as REINFORCE [ reference ] . In LeakGAN , we would hope the MANAGER module to capture some meaningful patterns . In this paper , we propose a sequence generation framework , called SeqGAN , to solve the problems . Modeling the data generator as a stochastic policy in reinforcement learning ( RL ) , SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update . The RL reward signal comes from the GAN discriminator judged on a complete sequence , and is passed back to the intermediate state - action steps using Monte Carlo search . Let be the parameters of the conditioned RNN , which we will also refer to as the actor . Our training algorithm is based on the following way of rewriting the gradient of the expected return : This equality is known in RL under the names policy gradient theorem sutton1999policy and stochastic actor - critic sutton1984temporal . The Q - network loss can be written as , where The policy network outputs an action . The policy parameters are updated using policy gradient ascent on the estimated Q - value , using gradient — note that this depends on the policy parameters only through the action that is input to the critic network . Further details of the Ape - X DPG algorithm are available in the appendix . We followed the same training procedure as that reported in the A3C paper with the following additional steps : We update our density model with the states generated by following the policy . During the policy gradient step , we compute the intrinsic rewards by querying the density model and add it to the extrinsic rewards before clipping them in the range as was done in the A3C paper . This resulted in minimal overhead in computation costs and the memory footprint was manageable ( 32 GB ) for most of the Atari games . We use a policy gradient algorithm to train the actor which makes use of our current estimate of . Let be the value function at some initial state , the policy gradient theorem says that , where refers to the gradient w.r.t . policy parameters Sutton00policygradient . We now consider several possible ways to estimate this gradient . The reward is the negative squared distance to the goal , and episodes terminate when the agent is within 0.01 of the goal or at the horizon of H = 100 . The policy was trained with MAML to maximize performance after 1 policy gradient update using 20 trajectories . Additional hyperparameter settings for this problem and the following RL problems are in Appendix A.2 . Ship data provided by ship automatic identification system ( AIS ) are used to train this path planning model . Subsequently , the improved DRL is obtained by combining DDPG with the artificial potential field . Finally , the path planning model is integrated into the electronic chart platform for experiments . In the presented digital twin framework , the active disturbance rejection controller ( ADRC ) is implemented for the pitch angle control of the WT plant in both SIL and HIL environments . The design of the ADRC controllers in the DT framework is accomplished by adopting deep deterministic policy gradient ( DDPG ) in two stages : ( i ) by employing a fitness evaluation of wind speed error , the internal coefficients of HIL controller are adjusted based on DDPG for the regulation of WT plant , and ( ii ) the difference between the rotor speed waveforms in HIL and SIL are reduced by DDPG to obtain a similar output behavior of the system in these environments . Some examinations based on DT are conducted to validate the effectiveness , high dynamic performance , robustness and adaptability of the suggested method in comparison to the prevalent state-of-the-art techniques . Deep-Q-network-based method was developed to solve these problems , but the notch filter parameter tuning is limited to discrete action spaces . This paper develops a new parameter selection method of notch filters , using the DDPG algorithm . DDPG algorithm , which is a model-free and actor-critic algorithm using deep neural networks , is utilized for its capability to operate over continuous action spaces . With MATPOWER , we achieve a general real-time OPF algorithm , which has the same effect to non-real time MATPOWER built-in OPF algorithm . DDPG algorithm is also applied on CloudPSS to solve the OPF problem , which combines the advantages of reinforcement learning and cloud simulation . A comprehensive comparison between DRL models and common supervised models is also provided . The results demonstrate that the proposed DDPG and RDPG models have obvious advantages in forecasting building energy consumption compared to common supervised models , while accounting for more computation time for model training . Their prediction performances measured by mean absolute error ( MAE ) can be improved by 16%-24 % for single-step ahead prediction , and 19%-32 % for multi-step ahead prediction . It can be combined with an arbitrary off-policy RL algorithm , such as DDPG or DQN , and their distributed versions . We build upon Ape-X DDPG and demonstrate our approach on robotic tight-fitting joint assembly tasks , based on force/torque and Cartesian pose observations . In particular , we run experiments on two different tasks : peg-in-hole and lap-joint . This paper investigates the resource allocation problem in vehicular communications based on multi-agent Deep Deterministic Policy Gradient ( DDPG ) , in which each Vehicle-to-Vehicle ( V2V ) communication acts as agent and adopts Non-Orthogonal Multiple Access ( NOMA ) technology to share the frequency spectrum that pre-allocated to Vehicle-to-Infrastructure ( V2I ) communications . Different with conventional D2D communications , the fast varying channel condition due to the high mobility in vehicular environment causes the difficulty of collecting instantaneous Channel State Information ( CSI ) at base station . Therefore , exploring the QoE-based offloading mechanism for better serving MUs in MENs is an urgent task . In this article , by reshaping the conventional offloading process , we explore a novel cooperative offloading mechanism in order to improve QoE. Our contributions can be identified in three aspects : the task-hub construction optimizes traditional MENs , which supports efficient MU management and low-latency communication ; the task preprocessing thoroughly refines the task inputs according to the priority and redundancy , resulting in higher QoE ; and the task scheduling with Deep Reinforcement Learning ( DRL ) named Double Dueling Deterministic Policy Gradient ( Double DDPG ) makes rational offloading policy that guarantees minimal service latency . Experimental results show that our proposed approach achieves much higher QoE performance than the existing schemes . The algorithm is tested on OpenAI gym ’s Robo school Inverted Pendulum Swing up-v1 environment . Once among 5 trial runs , reward obtained at the early stage of training in H- DDPG is higher than DDPG . In Hybrid update , the policy gradients are weighted by TD-error . Then , a deep deterministic policy gradient ( DDPG ) algorithm is adopted to adaptively adjust the baseline PI coefficients of the SIT2 controller by taking the benefits of the online learning and model-free features of reinforcement learning . In the DDPG structure , an actor-network determines the policy signals , while a critic-network evaluates the quality of the policy provided by the actor . The suggested DDPG algorithm naturally takes into account the baseline PI coefficients into the design objective and offers the SIT2-FPI structure with online coefficient adjusting ability through learning . To address this issue , dependent on the strong perception and decision-making ability of deep reinforcement learning ( DRL ) in automatic control problems , we propose a deep reinforcement learning based task scheduling method , which can dynamic assign submitted tasks to different VMs . In details , we formulate the task scheduling as a dynamical optimal problem with constraints , and then adopt the deep deterministic policy gradients ( DDPG ) network to find the optimal task assignment solution with meeting the Service Level Agreements ( SLAs ) requirements . It makes the optimal scheduling decision only dependent on learning directly from its experience without any prior knowledge . Moreover , we derive an MARL algorithm called Shapley Q-value deep deterministic policy gradient ( SQDDPG ) , using Shapley Q-value as the critic for each agent . We evaluate SQDDPG on Cooperative Navigation , Prey-and-Predator and Traffic Junction , compared with the state-of-the-art algorithms , e.g. , MADDPG , COMA , Independent DDPG and Independent A2C . In the experiments , SQDDPG shows a significant improvement on the convergence rate . This is complementary to distributing gradient computation , and the two approaches can be combined , but in this work we focus purely on data - generation . We use this distributed architecture to scale up variants of Deep Q - Networks ( DQN ) and Deep Deterministic Policy Gradient ( DDPG ) , and we evaluate these on the Arcade Learning Environment benchmark bellemare2013arcade , and on a range of continuous control tasks . Our architecture achieves a new state of the art performance on Atari games , using a fraction of the wall - clock time compared to the previous state of the art , and without per - game hyperparameter tuning . This is a complex problem , and so we model the system as a network of queues and formulate it as a Markov decision process over a continuous action space . This allows us to leverage the deep deterministic policy gradient algorithm from reinforcement learning to learn a delay minimizing scheduling policy under two scenarios : ( i ) an ideal setup where a centralized scheduler performs all per slot scheduling decisions and has full instantaneous knowledge of network state and ( ii ) again a centralized scheduler , but network state feedback and scheduling decisions are limited to once per frame , i.e. per many slots . For the second scenario , we model the scheduler by a recurrent neural network , which allows the agent to understand the evolution of the network state over the frame . In this paper , we explore for the first time in the smart grid context the benefits of using deep reinforcement learning , a hybrid type of methods that combines reinforcement learning with deep learning , to perform on-line optimization of schedules for building energy management systems . The learning procedure was explored using two methods , Deep Q-learning and deep policy gradient , both of which have been extended to perform multiple actions simultaneously . The proposed approach was validated on the large-scale Pecan Street Inc. database . We then provide an empirical comparison of these techniques with the remaining algorithmic details fixed , and show how different mixing of off-policy gradient estimates with on-policy samples contribute to improvements in empirical performance . The final algorithm provides a generalization and unification of existing deep policy gradient techniques , has theoretical guarantees on the bias introduced by off-policy updates , and improves on the state-of-the-art model-free deep RL methods on a number of OpenAI Gym continuous control benchmarks . This paper presents a study of a deep reinforcement learning technique that uses a Deep Deterministic Policy Gradient network for application in navigation of mobile robots . In order for the robot to arrive to a target on a map , the network has 10 laser range findings , the previous linear and angular velocity , and relative position and angle of the mobile robot to the target as inputs . Eventually , the simple controller can be discarded entirely . We show that not only does this technique train faster , it also is less sensitive to the structure of the DRL network and consistently outperforms a standard Deep Deterministic Policy Gradient network . We demonstrate the results in both simulation and real-world experiments . However , significant shortcomings were evident . Consequently , in a second set of experiments , a Deep Deterministic Policy Gradient learning model with continuous state and action spaces guided agent learning . Simulation results show that agents performed significantly better in terms of both sustainability and economic goals when using the second deep learning model . This paper proposes automating swing trading using deep reinforcement learning . The deep deterministic policy gradient-based neural network model trains to choose an action to sell , buy , or hold the stocks to maximize the gain in asset value . The paper also acknowledges the need for a system that predicts the trend in stock value to work along with the reinforcement learning algorithm . To address this problem , an optimal EV charging strategy in a distribution network is proposed to maximize the profit of the distribution system operators while satisfying all the physical constraints . When dealing with the uncertainties from EVs , a Markov decision process model is built to characterize the time series of the uncertainties , and then the deep deterministic policy gradient based reinforcement learning technique is utilized to analyze the impact of uncertainties on the charging strategy . Finally , numerical results verify the effectiveness of the proposed method . We study the problem of autotuning the memory allocation for applications running on modern distributed data processing systems . We show that an empirically-driven " white-box " algorithm , called RelM , that we have developed provides a close-to-optimal tuning at a fraction of the overheads compared to state-of-the-art AI-driven " black box " algorithms , namely , Bayesian Optimization ( BO ) and Deep Distributed Policy Gradient ( DDPG ) . The main reason for RelM 's superior performance is that the memory management in modern memory-based data analytics systems is an interplay of algorithms at multiple levels : ( i ) at the resource-management level across various containers allocated by resource managers like Kubernetes and YARN , ( ii ) at the container level among the OS , pods , and processes such as the Java Virtual Machine ( JVM ) , ( iii ) at the application level for caching , aggregation , data shuffles , and application data structures , and ( iv ) at the JVM level across various pools such as the Young and Old Generation . Currently , deep reinforcement learning is rising as a powerful tool to enable each node in the network to have a real-time self-organising ability . In this paper , we present two novel approaches based on deep deterministic policy gradient algorithm , namely “ distributed deep deterministic policy gradient ” and “ sharing deep deterministic policy gradient ” , for the multi-agent power allocation problem in D2D-based V2V communications . Numerical results show that our proposed models outperform other deep reinforcement learning approaches in terms of the network ’s energy efficiency and flexibility . This is complementary to distributing gradient computation , and the two approaches can be combined , but in this work we focus purely on data - generation . We use this distributed architecture to scale up variants of Deep Q - Networks ( DQN ) and Deep Deterministic Policy Gradient ( DDPG ) , and we evaluate these on the Arcade Learning Environment benchmark bellemare2013arcade , and on a range of continuous control tasks . Our architecture achieves a new state of the art performance on Atari games , using a fraction of the wall - clock time compared to the previous state of the art , and without per - game hyperparameter tuning . We optimize for di erent tracking metrics directly while propagat- ing covariance information about each object 's state . We use trust region policy optimization ( TRPO ) to train a shared policy across all agents , parameterized by a multi-layer neural network . Our ex- periments show an improvement in tracking accuracy over similar state-of-the-art , rule-based approaches on a popular multi-object tracking dataset . After providing a theoretical analysis , we propose a practical method called trust region policy optimization ( TRPO ) , which performs well on two challenging tasks : simulated robotic locomotion , and playing Atari games using screen images as input . Chapter 4 looks at improving sample complexity of policy gradient methods in a way that is complementary to TRPO : reducing the variance of policy gradient estimates using a state-value function . Using this method , we obtain state-of-the-art results for learning locomotion controllers for simulated 3D robots . The agent receives reward from the environment to find an optimal policy that maximises the reward . Trust Region Policy Optimisation ( TRPO ) is a recent policy optimisation algorithm that achieves superior results in various RL benchmarks , but is computationally expensive . This paper proposes Customised Pearlmutter Propagation ( CPP ) , a novel hardware architecture that accelerates TRPO on FPGA . Trust Region Policy Optimization ( TRPO ) and Proximal Policy Optimization ( PPO ) are among the most successful policy gradient approaches in deep reinforcement learning ( RL ) . While these methods achieve state-of-the-art performance across a wide range of challenging tasks , there is room for improvement in the stabilization of the policy learning and how the off-policy data are used . We analyze the connection between Q-Prop and existing model-free algorithms , and use control variate theory to derive two variants of Q-Prop with conservative and aggressive adaptation . We show that conservative Q-Prop provides substantial gains in sample efficiency over trust region policy optimization ( TRPO ) with generalized advantage estimation ( GAE ) , and improves stability over deep deterministic policy gradient ( DDPG ) , the state-of-the-art on-policy and off-policy methods , on OpenAI Gym 's MuJoCo continuous control environments . As reinforcement learning continues to drive machine intelligence beyond its conventional boundary , unsubstantial practices in sparse reward environment severely limit further applications in a broader range of advanced fields . Motivated by the demand for an effective deep reinforcement learning algorithm that accommodates sparse reward environment , this paper presents Hindsight Trust Region Policy Optimization ( Hindsight TRPO ) , a method that efficiently utilizes interactions in sparse reward conditions and maintains learning stability by restricting variance during the policy update process . Firstly , the hindsight methodology is expanded to TRPO , an advanced and efficient on-policy policy gradient method . Then , under the condition that the distributions are close , the KL-divergence is appropriately approximated by another $ f$-divergence . Such approximation results in the decrease of variance during KL-divergence estimation and alleviates the instability during policy update . Experimental results on both discrete and continuous benchmark tasks demonstrate that Hindsight TRPO converges steadily and significantly faster than previous policy gradient methods . It achieves effective performances and high data-efficiency for training policies in sparse reward environments . To update the control policy during learning , we use Trust Region Policy Optimization with Generalized Advantage Function ( TRPOGAE ) . We propose a modification to the gradient update stage of TRPO to better accommodate multi-task learning scenarios . We evaluate our approach on three continuous motor skill learning problems in simulation : 1 ) a locomotion task where three single legged robots with considerable difference in shape and size are trained to hop forward , 2 ) a manipulation task where three robot manipulators with different sizes and joint types are trained to reach different locations in 3D space , and 3 ) locomotion of a two-legged robot , whose range of motion of one leg is constrained in different ways . Bio-inspired artificial intelligence — reinforcement learning , artificial neural network and bio-inspired methods— is used in this paper in order to design an algorithm capable of learning to flight autonomously . In this paper , we introduced and explained about our algorithm called FE-TRPO ( Flight Enhanced TRPO ) and tested it in Gazebo simulator by an AscTec Pelican simulated model where the multi-rotor shows the capability of learning to fly by learning from its own actions and the rewards receiving from the environment in an autonomous way , where the only controller is the reinforcement learning based algorithm and no conventional controllers such as PID , MPC , etc is used . Finally , our algorithm learns to fly using only the data generated from IMU . The optimal process parameters for Am extraction by TRPO are : 10 stages for extraction and 2 stages for scrubbing ; a flow rate ratio of 0.931 for extraction and 4.42 for scrubbing ; nitric acid concentration of 1.35mol/L for the feed and 0.5mol/L for the scrubbing solution . Finally , the nitric acid and Am concentration profiles in the optimal TRPO extraction process are given . We first analyze TRPO in the planning setting , in which we have access to the model and the entire state space . Then , we consider sample-based TRPO and establish $ \tilde O(1/\sqrt{N})$ convergence rate to the global optimum . Importantly , the adaptive scaling mechanism allows us to analyze TRPO in { \em regularized MDPs } for which we prove fast rates of $ \tilde O(1/N)$ , much like results in convex optimization . Table [ reference ] compares our evaluation scores to Double DQN ( DDQN ) , Double DQN with pseudocount ( DDQN - PC ) , A3C + , DQN Pop - Art ( DQN - PA ) , Dueling Network ( Dueling ) , Gorila , DQN with Model Prediction Exploration Bonuses ( MP - EB ) , Trust Region Policy Optimisation ( TRPO ) , and TRPO - AE - SimHash ( TRPO - Hash ) . The most interesting comparisons for our purposes are with TRPO - Hash , DDQN - PC , A3C + , and MP - EB , because these algorithms all use exploration strategies that drive the agent to reduce its uncertainty . TRPO - Hash , DDQN - PC , and In this paper , we look specifically into meta-model based algorithms that stochastically drive global search through an optimal sampling criteria evaluated over a constructed meta-model of the predicted response considering the uncertainty of the response . We propose Trust Region Based Optimization with Adaptive Restart ( TBOAR ) , a family of algorithms that dynamically restarts a trust region based search method via an optimal sampling criteria derived upon a meta-model based global search approach . Additionally , we propose a new sampling criteria to reconcile undesirable adaptive restart trajectories . Extensive numerical results demonstrate that it provides consistently better accuracy than POD-TPWL , with speedups of about two orders of magnitude relative to high-fidelity simulations for the problems considered . We demonstrate that POD-TPWQ can be used as an error estimator for POD-TPWL , which motivates the development of a trust-region-based optimization framework . This procedure uses POD-TPWL for fast function evaluations and a POD-TPWQ error estimator to determine when retraining , which entails a high-fidelity simulation , is required . We show that WD regularizers acting on appropriate policy embeddings efficiently incorporate behavioral characteristics into policy optimization . We demonstrate that they improve Evolution Strategy methods by encouraging more efficient exploration , can be applied in imitation learning and to speed up training of Trust Region Policy Optimization methods . Since the exact computation of WDs is expensive , we develop approximate algorithms based on the combination of different methods : dual formulation of the optimal transport problem , alternating optimization and random feature maps , to effectively replace exact WD computations in the RL tasks considered . In this paper an approach to building metamodels in sub-spaces while running the optimization problem in the full space is proposed . This technique is implemented using the Moving Least Squares metamodels within a trust region based optimization scheme known as the Multipoint Approximation Method and demonstrated by an analytical example and a small-scale FE test example . Table [ reference ] compares our evaluation scores to Double DQN ( DDQN ) , Double DQN with pseudocount ( DDQN - PC ) , A3C + , DQN Pop - Art ( DQN - PA ) , Dueling Network ( Dueling ) , Gorila , DQN with Model Prediction Exploration Bonuses ( MP - EB ) , Trust Region Policy Optimisation ( TRPO ) , and TRPO - AE - SimHash ( TRPO - Hash ) . The most interesting comparisons for our purposes are with TRPO - Hash , DDQN - PC , A3C + , and MP - EB , because these algorithms all use exploration strategies that drive the agent to reduce its uncertainty . We propose deep Reinforcement Learning ( RL ) algorithms inspired by mirror descent , a well-known first-order trust region optimization method for solving constrained convex problems . Our approach , which we call as Mirror Descent Policy Optimization ( MDPO ) , is based on the idea of iteratively solving a ` trust-region ' problem that minimizes a sum of two terms : a linearization of the objective function and a proximity term that restricts two consecutive updates to be close to each other . Following this approach we derive on-policy and off-policy variants of the MDPO algorithm and analyze their performance while emphasizing important implementation details , motivated by the existing theoretical framework . We highlight the connections between on-policy MDPO and two popular trust region RL algorithms : TRPO and PPO , and conduct a comprehensive empirical comparison of these algorithms . We then derive off-policy MDPO and compare its performance to existing approaches . Importantly , we show that the theoretical framework of MDPO can be scaled to deep RL while achieving good performance on popular benchmarks .
Reinforcement learning algorithms that use policy gradient methods approach an optimal policy faster than Q-learning but at the cost of incurring high variances in gradients . Among variance reduction techniques are actor-critic methods that use value and advantage functions to train a policy actor .
Policy Gradient methods that explore directly in parameter space are among the most effective and robust direct policy search methods and have drawn a lot of attention lately . The basic method from this field , Policy Gradients with Parameter-based Exploration , uses two samples that are symmetric around the current hypothesis to circumvent misleading reward in asymmetrical reward distributed problems gathered with the usual baseline approach .
A number of adversarial learning methods have been proposed to learn the reward function together with the dialog policy . However , to alternatively update the dialog policy and the reward model on the fly , the algorithms to update the dialog policy are limited to policy gradient-based algorithms , such as REINFORCE and PPO . Besides , the alternative training of the dialog agent and the reward model can easily get stuck in local optimum or result in mode collapse .
This paper sets forth a framework for deep reinforcement learning as applied to market making ( DRLMM ) for cryptocurrencies . Two advanced policy gradient-based algorithms were selected as agents to interact with an environment that represents the observation space through limit order book data , and order flow arrival statistics . Within the experiment , a forward-feed neural network is used as the function approximator and two reward functions are compared .
We show that usual likelihood ratio methods used in discrete-time , fail to proceed the gradient because they are subject to variance explosion when the discretization time-step decreases to 0 . We describe an alternative approach based on the approximation of the pathwise derivative , which leads to a policy gradient estimate that converges almost surely to the true gradient when the time-step tends to 0 . The underlying idea starts with the derivation of an explicit representation of the policy gradient using pathwise derivation .
Under this framework , the equivalence of timeout and stochastic policies on power-performance tradeoff is revealed , and the equivalent relation between these two types of DPM policy is derived . Then , a reinforcement learning algorithm that combines policy gradient estimate and stochastic approximation is proposed for optimising timeout policy online . This algorithm does not depend on any prior knowledge of system parameters , and can achieve a global optimum with less computational cost .
However , this strategy has numerous problems , including variations between the populations being studied , different methods of data collection , and secular trends . Two analyses that are useful for policy changes and do not rely on external control groups are interrupted time-series analysis and policy gradient analysis . Interrupted Time-Series Analysis A common design used for evaluating policy change compares study outcomes during a specified period preceding the change with outcomes during a similar period after the change .
With the soft gradient policy theorem , we construct the maximum entropy reinforcement learning framework for on-policy RL . For policy gradient based on-policy algorithms , policy network is often represented as Gaussian distribution with the action variance restricted to be global for all the states observed from the environment . We propose an idea called action variance scale for policy network and find it can work collaboratively with the idea of entropy regularization .
BLRL starts with an initial set of parameterized boundaries that partition the state space into distinct control modes . Reinforcement reward is used to update the boundary parameters using the policy gradient formulation of Sutton et al. ( 2000 ) . We demonstrate that stochastic search can be limited to regions near mode boundaries , thus greatly reducing search , while still guaranteeing convergence to a locally optimal deterministic mode switching policy .
Instead of directly maximizing rewards , GASIL focuses on reproducing past good trajectories , which can potentially make long-term credit assignment easier when rewards are sparse and delayed . GASIL can be easily combined with any policy gradient objective by using GASIL as a learned shaped reward function . Our experimental results show that GASIL improves the performance of proximal policy optimization on 2D Point Mass and MuJoCo environments with delayed reward and stochastic dynamics .
This paper presents PolicyGAN , a policy gradient paradigm for training Generative Adversarial Networks that views the generator as an image generation neural agent which is rewarded by another neural agent , termed as the discriminator . Rewards are higher for samples near the original data manifold .
We study a model of neuronal specialization using a policy gradient reinforcement approach . ( 1 ) The neurons stochastically fire according to their synaptic input plus a noise term ; ( 2 ) The environment is a closed-loop system composed of a rotating eye and a visual punctual target ; ( 3 ) The network is composed of a foveated retina , a primary layer and a motoneuron layer ; ( 4 ) The reward depends on the distance between the subjective target position and the fovea and ( 5 ) the weight update depends on a Hebbian trace defined according to a policy gradient principle . In order to take into account the mismatch between neuronal and environmental integration times , we distort the firing probability with a " pink noise " term whose autocorrelation is of the order of 100 ms , so that the firing probability is overestimated ( or underestimated ) for about 100 ms periods .
( iii ) The gradients of the Lagrangian associated with the safe-learning problem can be easily computed using standard policy gradient results and stochastic approximation tools . Leveraging these advantages , we establish that primal-dual algorithms are able to find policies that are safe and optimal .
However the framework is also suitable for discrete units , and the only modification needed is to replace the Gaussian with a multinomial parameterised by the outputs of a softmax function . Though the reparameterisation trick for continuous variables is not applicable for this case , a policy gradient approach mnih2014neural can help to alleviate the high variance problem during stochastic estimation . proposed a variational inference framework for semi - supervised learning , but the prior distribution over the hidden variable remains as the standard Gaussian prior , while we apply a conditional parameterised Gaussian distribution , which is jointly learned with the variational distribution .
We now discuss how to train the model parameters ( including and ) from a training dataset using reinforcement learning . One approach is the policy gradient method ( REINFORCE ) , which uses the current policy to roll out multiple trajectories to estimate a stochastic gradient , and then updates the policy via stochastic gradient ascent . Previous RL - based KBC methods typically use REINFORCE to learn the policy .
Notice that the above procedure is fully differentiable . One can train G θ in an end - to - end manner using a policy gradient algorithm such as REINFORCE [ reference ] . In LeakGAN , we would hope the MANAGER module to capture some meaningful patterns .
In this paper , we propose a sequence generation framework , called SeqGAN , to solve the problems . Modeling the data generator as a stochastic policy in reinforcement learning ( RL ) , SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update . The RL reward signal comes from the GAN discriminator judged on a complete sequence , and is passed back to the intermediate state - action steps using Monte Carlo search .
Let be the parameters of the conditioned RNN , which we will also refer to as the actor . Our training algorithm is based on the following way of rewriting the gradient of the expected return : This equality is known in RL under the names policy gradient theorem sutton1999policy and stochastic actor - critic sutton1984temporal .
The Q - network loss can be written as , where The policy network outputs an action . The policy parameters are updated using policy gradient ascent on the estimated Q - value , using gradient — note that this depends on the policy parameters only through the action that is input to the critic network . Further details of the Ape - X DPG algorithm are available in the appendix .
We followed the same training procedure as that reported in the A3C paper with the following additional steps : We update our density model with the states generated by following the policy . During the policy gradient step , we compute the intrinsic rewards by querying the density model and add it to the extrinsic rewards before clipping them in the range as was done in the A3C paper . This resulted in minimal overhead in computation costs and the memory footprint was manageable ( 32 GB ) for most of the Atari games .
We use a policy gradient algorithm to train the actor which makes use of our current estimate of . Let be the value function at some initial state , the policy gradient theorem says that , where refers to the gradient w.r.t . policy parameters Sutton00policygradient . We now consider several possible ways to estimate this gradient .
The reward is the negative squared distance to the goal , and episodes terminate when the agent is within 0.01 of the goal or at the horizon of H = 100 . The policy was trained with MAML to maximize performance after 1 policy gradient update using 20 trajectories . Additional hyperparameter settings for this problem and the following RL problems are in Appendix A.2 .
Ship data provided by ship automatic identification system ( AIS ) are used to train this path planning model . Subsequently , the improved DRL is obtained by combining DDPG with the artificial potential field . Finally , the path planning model is integrated into the electronic chart platform for experiments .
In the presented digital twin framework , the active disturbance rejection controller ( ADRC ) is implemented for the pitch angle control of the WT plant in both SIL and HIL environments . The design of the ADRC controllers in the DT framework is accomplished by adopting deep deterministic policy gradient ( DDPG ) in two stages : ( i ) by employing a fitness evaluation of wind speed error , the internal coefficients of HIL controller are adjusted based on DDPG for the regulation of WT plant , and ( ii ) the difference between the rotor speed waveforms in HIL and SIL are reduced by DDPG to obtain a similar output behavior of the system in these environments . Some examinations based on DT are conducted to validate the effectiveness , high dynamic performance , robustness and adaptability of the suggested method in comparison to the prevalent state-of-the-art techniques .
Deep-Q-network-based method was developed to solve these problems , but the notch filter parameter tuning is limited to discrete action spaces . This paper develops a new parameter selection method of notch filters , using the DDPG algorithm . DDPG algorithm , which is a model-free and actor-critic algorithm using deep neural networks , is utilized for its capability to operate over continuous action spaces .
With MATPOWER , we achieve a general real-time OPF algorithm , which has the same effect to non-real time MATPOWER built-in OPF algorithm . DDPG algorithm is also applied on CloudPSS to solve the OPF problem , which combines the advantages of reinforcement learning and cloud simulation .
A comprehensive comparison between DRL models and common supervised models is also provided . The results demonstrate that the proposed DDPG and RDPG models have obvious advantages in forecasting building energy consumption compared to common supervised models , while accounting for more computation time for model training . Their prediction performances measured by mean absolute error ( MAE ) can be improved by 16%-24 % for single-step ahead prediction , and 19%-32 % for multi-step ahead prediction .
It can be combined with an arbitrary off-policy RL algorithm , such as DDPG or DQN , and their distributed versions . We build upon Ape-X DDPG and demonstrate our approach on robotic tight-fitting joint assembly tasks , based on force/torque and Cartesian pose observations . In particular , we run experiments on two different tasks : peg-in-hole and lap-joint .
This paper investigates the resource allocation problem in vehicular communications based on multi-agent Deep Deterministic Policy Gradient ( DDPG ) , in which each Vehicle-to-Vehicle ( V2V ) communication acts as agent and adopts Non-Orthogonal Multiple Access ( NOMA ) technology to share the frequency spectrum that pre-allocated to Vehicle-to-Infrastructure ( V2I ) communications . Different with conventional D2D communications , the fast varying channel condition due to the high mobility in vehicular environment causes the difficulty of collecting instantaneous Channel State Information ( CSI ) at base station .
Therefore , exploring the QoE-based offloading mechanism for better serving MUs in MENs is an urgent task . In this article , by reshaping the conventional offloading process , we explore a novel cooperative offloading mechanism in order to improve QoE. Our contributions can be identified in three aspects : the task-hub construction optimizes traditional MENs , which supports efficient MU management and low-latency communication ; the task preprocessing thoroughly refines the task inputs according to the priority and redundancy , resulting in higher QoE ; and the task scheduling with Deep Reinforcement Learning ( DRL ) named Double Dueling Deterministic Policy Gradient ( Double DDPG ) makes rational offloading policy that guarantees minimal service latency . Experimental results show that our proposed approach achieves much higher QoE performance than the existing schemes .
The algorithm is tested on OpenAI gym ’s Robo school Inverted Pendulum Swing up-v1 environment . Once among 5 trial runs , reward obtained at the early stage of training in H- DDPG is higher than DDPG . In Hybrid update , the policy gradients are weighted by TD-error .
Then , a deep deterministic policy gradient ( DDPG ) algorithm is adopted to adaptively adjust the baseline PI coefficients of the SIT2 controller by taking the benefits of the online learning and model-free features of reinforcement learning . In the DDPG structure , an actor-network determines the policy signals , while a critic-network evaluates the quality of the policy provided by the actor . The suggested DDPG algorithm naturally takes into account the baseline PI coefficients into the design objective and offers the SIT2-FPI structure with online coefficient adjusting ability through learning .
To address this issue , dependent on the strong perception and decision-making ability of deep reinforcement learning ( DRL ) in automatic control problems , we propose a deep reinforcement learning based task scheduling method , which can dynamic assign submitted tasks to different VMs . In details , we formulate the task scheduling as a dynamical optimal problem with constraints , and then adopt the deep deterministic policy gradients ( DDPG ) network to find the optimal task assignment solution with meeting the Service Level Agreements ( SLAs ) requirements . It makes the optimal scheduling decision only dependent on learning directly from its experience without any prior knowledge .
Moreover , we derive an MARL algorithm called Shapley Q-value deep deterministic policy gradient ( SQDDPG ) , using Shapley Q-value as the critic for each agent . We evaluate SQDDPG on Cooperative Navigation , Prey-and-Predator and Traffic Junction , compared with the state-of-the-art algorithms , e.g. , MADDPG , COMA , Independent DDPG and Independent A2C . In the experiments , SQDDPG shows a significant improvement on the convergence rate .
This is complementary to distributing gradient computation , and the two approaches can be combined , but in this work we focus purely on data - generation . We use this distributed architecture to scale up variants of Deep Q - Networks ( DQN ) and Deep Deterministic Policy Gradient ( DDPG ) , and we evaluate these on the Arcade Learning Environment benchmark bellemare2013arcade , and on a range of continuous control tasks . Our architecture achieves a new state of the art performance on Atari games , using a fraction of the wall - clock time compared to the previous state of the art , and without per - game hyperparameter tuning .
This is a complex problem , and so we model the system as a network of queues and formulate it as a Markov decision process over a continuous action space . This allows us to leverage the deep deterministic policy gradient algorithm from reinforcement learning to learn a delay minimizing scheduling policy under two scenarios : ( i ) an ideal setup where a centralized scheduler performs all per slot scheduling decisions and has full instantaneous knowledge of network state and ( ii ) again a centralized scheduler , but network state feedback and scheduling decisions are limited to once per frame , i.e. per many slots . For the second scenario , we model the scheduler by a recurrent neural network , which allows the agent to understand the evolution of the network state over the frame .
In this paper , we explore for the first time in the smart grid context the benefits of using deep reinforcement learning , a hybrid type of methods that combines reinforcement learning with deep learning , to perform on-line optimization of schedules for building energy management systems . The learning procedure was explored using two methods , Deep Q-learning and deep policy gradient , both of which have been extended to perform multiple actions simultaneously . The proposed approach was validated on the large-scale Pecan Street Inc. database .
We then provide an empirical comparison of these techniques with the remaining algorithmic details fixed , and show how different mixing of off-policy gradient estimates with on-policy samples contribute to improvements in empirical performance . The final algorithm provides a generalization and unification of existing deep policy gradient techniques , has theoretical guarantees on the bias introduced by off-policy updates , and improves on the state-of-the-art model-free deep RL methods on a number of OpenAI Gym continuous control benchmarks .
This paper presents a study of a deep reinforcement learning technique that uses a Deep Deterministic Policy Gradient network for application in navigation of mobile robots . In order for the robot to arrive to a target on a map , the network has 10 laser range findings , the previous linear and angular velocity , and relative position and angle of the mobile robot to the target as inputs .
Eventually , the simple controller can be discarded entirely . We show that not only does this technique train faster , it also is less sensitive to the structure of the DRL network and consistently outperforms a standard Deep Deterministic Policy Gradient network . We demonstrate the results in both simulation and real-world experiments .
However , significant shortcomings were evident . Consequently , in a second set of experiments , a Deep Deterministic Policy Gradient learning model with continuous state and action spaces guided agent learning . Simulation results show that agents performed significantly better in terms of both sustainability and economic goals when using the second deep learning model .
This paper proposes automating swing trading using deep reinforcement learning . The deep deterministic policy gradient-based neural network model trains to choose an action to sell , buy , or hold the stocks to maximize the gain in asset value . The paper also acknowledges the need for a system that predicts the trend in stock value to work along with the reinforcement learning algorithm .
To address this problem , an optimal EV charging strategy in a distribution network is proposed to maximize the profit of the distribution system operators while satisfying all the physical constraints . When dealing with the uncertainties from EVs , a Markov decision process model is built to characterize the time series of the uncertainties , and then the deep deterministic policy gradient based reinforcement learning technique is utilized to analyze the impact of uncertainties on the charging strategy . Finally , numerical results verify the effectiveness of the proposed method .
We study the problem of autotuning the memory allocation for applications running on modern distributed data processing systems . We show that an empirically-driven " white-box " algorithm , called RelM , that we have developed provides a close-to-optimal tuning at a fraction of the overheads compared to state-of-the-art AI-driven " black box " algorithms , namely , Bayesian Optimization ( BO ) and Deep Distributed Policy Gradient ( DDPG ) . The main reason for RelM 's superior performance is that the memory management in modern memory-based data analytics systems is an interplay of algorithms at multiple levels : ( i ) at the resource-management level across various containers allocated by resource managers like Kubernetes and YARN , ( ii ) at the container level among the OS , pods , and processes such as the Java Virtual Machine ( JVM ) , ( iii ) at the application level for caching , aggregation , data shuffles , and application data structures , and ( iv ) at the JVM level across various pools such as the Young and Old Generation .
Currently , deep reinforcement learning is rising as a powerful tool to enable each node in the network to have a real-time self-organising ability . In this paper , we present two novel approaches based on deep deterministic policy gradient algorithm , namely “ distributed deep deterministic policy gradient ” and “ sharing deep deterministic policy gradient ” , for the multi-agent power allocation problem in D2D-based V2V communications . Numerical results show that our proposed models outperform other deep reinforcement learning approaches in terms of the network ’s energy efficiency and flexibility .
This is complementary to distributing gradient computation , and the two approaches can be combined , but in this work we focus purely on data - generation . We use this distributed architecture to scale up variants of Deep Q - Networks ( DQN ) and Deep Deterministic Policy Gradient ( DDPG ) , and we evaluate these on the Arcade Learning Environment benchmark bellemare2013arcade , and on a range of continuous control tasks . Our architecture achieves a new state of the art performance on Atari games , using a fraction of the wall - clock time compared to the previous state of the art , and without per - game hyperparameter tuning .
We optimize for di erent tracking metrics directly while propagat- ing covariance information about each object 's state . We use trust region policy optimization ( TRPO ) to train a shared policy across all agents , parameterized by a multi-layer neural network . Our ex- periments show an improvement in tracking accuracy over similar state-of-the-art , rule-based approaches on a popular multi-object tracking dataset .
After providing a theoretical analysis , we propose a practical method called trust region policy optimization ( TRPO ) , which performs well on two challenging tasks : simulated robotic locomotion , and playing Atari games using screen images as input . Chapter 4 looks at improving sample complexity of policy gradient methods in a way that is complementary to TRPO : reducing the variance of policy gradient estimates using a state-value function . Using this method , we obtain state-of-the-art results for learning locomotion controllers for simulated 3D robots .
The agent receives reward from the environment to find an optimal policy that maximises the reward . Trust Region Policy Optimisation ( TRPO ) is a recent policy optimisation algorithm that achieves superior results in various RL benchmarks , but is computationally expensive . This paper proposes Customised Pearlmutter Propagation ( CPP ) , a novel hardware architecture that accelerates TRPO on FPGA .
Trust Region Policy Optimization ( TRPO ) and Proximal Policy Optimization ( PPO ) are among the most successful policy gradient approaches in deep reinforcement learning ( RL ) . While these methods achieve state-of-the-art performance across a wide range of challenging tasks , there is room for improvement in the stabilization of the policy learning and how the off-policy data are used .
We analyze the connection between Q-Prop and existing model-free algorithms , and use control variate theory to derive two variants of Q-Prop with conservative and aggressive adaptation . We show that conservative Q-Prop provides substantial gains in sample efficiency over trust region policy optimization ( TRPO ) with generalized advantage estimation ( GAE ) , and improves stability over deep deterministic policy gradient ( DDPG ) , the state-of-the-art on-policy and off-policy methods , on OpenAI Gym 's MuJoCo continuous control environments .
As reinforcement learning continues to drive machine intelligence beyond its conventional boundary , unsubstantial practices in sparse reward environment severely limit further applications in a broader range of advanced fields . Motivated by the demand for an effective deep reinforcement learning algorithm that accommodates sparse reward environment , this paper presents Hindsight Trust Region Policy Optimization ( Hindsight TRPO ) , a method that efficiently utilizes interactions in sparse reward conditions and maintains learning stability by restricting variance during the policy update process . Firstly , the hindsight methodology is expanded to TRPO , an advanced and efficient on-policy policy gradient method . Then , under the condition that the distributions are close , the KL-divergence is appropriately approximated by another $ f$-divergence . Such approximation results in the decrease of variance during KL-divergence estimation and alleviates the instability during policy update . Experimental results on both discrete and continuous benchmark tasks demonstrate that Hindsight TRPO converges steadily and significantly faster than previous policy gradient methods . It achieves effective performances and high data-efficiency for training policies in sparse reward environments .
To update the control policy during learning , we use Trust Region Policy Optimization with Generalized Advantage Function ( TRPOGAE ) . We propose a modification to the gradient update stage of TRPO to better accommodate multi-task learning scenarios . We evaluate our approach on three continuous motor skill learning problems in simulation : 1 ) a locomotion task where three single legged robots with considerable difference in shape and size are trained to hop forward , 2 ) a manipulation task where three robot manipulators with different sizes and joint types are trained to reach different locations in 3D space , and 3 ) locomotion of a two-legged robot , whose range of motion of one leg is constrained in different ways .
Bio-inspired artificial intelligence — reinforcement learning , artificial neural network and bio-inspired methods— is used in this paper in order to design an algorithm capable of learning to flight autonomously . In this paper , we introduced and explained about our algorithm called FE-TRPO ( Flight Enhanced TRPO ) and tested it in Gazebo simulator by an AscTec Pelican simulated model where the multi-rotor shows the capability of learning to fly by learning from its own actions and the rewards receiving from the environment in an autonomous way , where the only controller is the reinforcement learning based algorithm and no conventional controllers such as PID , MPC , etc is used . Finally , our algorithm learns to fly using only the data generated from IMU .
The optimal process parameters for Am extraction by TRPO are : 10 stages for extraction and 2 stages for scrubbing ; a flow rate ratio of 0.931 for extraction and 4.42 for scrubbing ; nitric acid concentration of 1.35mol/L for the feed and 0.5mol/L for the scrubbing solution . Finally , the nitric acid and Am concentration profiles in the optimal TRPO extraction process are given .
We first analyze TRPO in the planning setting , in which we have access to the model and the entire state space . Then , we consider sample-based TRPO and establish $ \tilde O(1/\sqrt{N})$ convergence rate to the global optimum . Importantly , the adaptive scaling mechanism allows us to analyze TRPO in { \em regularized MDPs } for which we prove fast rates of $ \tilde O(1/N)$ , much like results in convex optimization .
Table [ reference ] compares our evaluation scores to Double DQN ( DDQN ) , Double DQN with pseudocount ( DDQN - PC ) , A3C + , DQN Pop - Art ( DQN - PA ) , Dueling Network ( Dueling ) , Gorila , DQN with Model Prediction Exploration Bonuses ( MP - EB ) , Trust Region Policy Optimisation ( TRPO ) , and TRPO - AE - SimHash ( TRPO - Hash ) . The most interesting comparisons for our purposes are with TRPO - Hash , DDQN - PC , A3C + , and MP - EB , because these algorithms all use exploration strategies that drive the agent to reduce its uncertainty . TRPO - Hash , DDQN - PC , and
In this paper , we look specifically into meta-model based algorithms that stochastically drive global search through an optimal sampling criteria evaluated over a constructed meta-model of the predicted response considering the uncertainty of the response . We propose Trust Region Based Optimization with Adaptive Restart ( TBOAR ) , a family of algorithms that dynamically restarts a trust region based search method via an optimal sampling criteria derived upon a meta-model based global search approach . Additionally , we propose a new sampling criteria to reconcile undesirable adaptive restart trajectories .
Extensive numerical results demonstrate that it provides consistently better accuracy than POD-TPWL , with speedups of about two orders of magnitude relative to high-fidelity simulations for the problems considered . We demonstrate that POD-TPWQ can be used as an error estimator for POD-TPWL , which motivates the development of a trust-region-based optimization framework . This procedure uses POD-TPWL for fast function evaluations and a POD-TPWQ error estimator to determine when retraining , which entails a high-fidelity simulation , is required .
We show that WD regularizers acting on appropriate policy embeddings efficiently incorporate behavioral characteristics into policy optimization . We demonstrate that they improve Evolution Strategy methods by encouraging more efficient exploration , can be applied in imitation learning and to speed up training of Trust Region Policy Optimization methods . Since the exact computation of WDs is expensive , we develop approximate algorithms based on the combination of different methods : dual formulation of the optimal transport problem , alternating optimization and random feature maps , to effectively replace exact WD computations in the RL tasks considered .
In this paper an approach to building metamodels in sub-spaces while running the optimization problem in the full space is proposed . This technique is implemented using the Moving Least Squares metamodels within a trust region based optimization scheme known as the Multipoint Approximation Method and demonstrated by an analytical example and a small-scale FE test example .
Table [ reference ] compares our evaluation scores to Double DQN ( DDQN ) , Double DQN with pseudocount ( DDQN - PC ) , A3C + , DQN Pop - Art ( DQN - PA ) , Dueling Network ( Dueling ) , Gorila , DQN with Model Prediction Exploration Bonuses ( MP - EB ) , Trust Region Policy Optimisation ( TRPO ) , and TRPO - AE - SimHash ( TRPO - Hash ) . The most interesting comparisons for our purposes are with TRPO - Hash , DDQN - PC , A3C + , and MP - EB , because these algorithms all use exploration strategies that drive the agent to reduce its uncertainty .
We propose deep Reinforcement Learning ( RL ) algorithms inspired by mirror descent , a well-known first-order trust region optimization method for solving constrained convex problems . Our approach , which we call as Mirror Descent Policy Optimization ( MDPO ) , is based on the idea of iteratively solving a ` trust-region ' problem that minimizes a sum of two terms : a linearization of the objective function and a proximity term that restricts two consecutive updates to be close to each other . Following this approach we derive on-policy and off-policy variants of the MDPO algorithm and analyze their performance while emphasizing important implementation details , motivated by the existing theoretical framework . We highlight the connections between on-policy MDPO and two popular trust region RL algorithms : TRPO and PPO , and conduct a comprehensive empirical comparison of these algorithms . We then derive off-policy MDPO and compare its performance to existing approaches . Importantly , we show that the theoretical framework of MDPO can be scaled to deep RL while achieving good performance on popular benchmarks .
Stabilizing Actor Policies by Approximating Advantage Distributions from K Critics Baseline-Free Sampling in Parameter Exploring Policy Gradients: Super Symmetric PGPE Guided Dialog Policy Learning without Adversarial Learning in the Loop Deep Reinforcement Learning in Cryptocurrency Market Making Policy Gradient in Continuous Time Adaptive optimisation of timeout policy for dynamic power management based on semi-Markov control processes Policy and Program Analysis Using Administrative Databases On-policy Reinforcement Learning with Entropy Regularization Localizing Search in Reinforcement Learning Generative Adversarial Self-Imitation Learning PolicyGAN: Training generative adversarial networks using policy gradient A Model of Neuronal Specialization Using Hebbian Policy-Gradient with "Slow" Noise Safe Policies for Reinforcement Learning via Primal-Dual Methods Neural Variational Inference for Text Processing M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search Long Text Generation via Adversarial Training with Leaked Information SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient An Actor-Critic Algorithm for Sequence Prediction Distributed Prioritized Experience Replay Unifying Count-Based Exploration and Intrinsic Motivation The Reactor: A Sample-Efficient Actor-Critic Architecture Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks An Autonomous Path Planning Model for Unmanned Ships Based on Deep Reinforcement Learning Digital Twins-Assisted Design of Next-Generation Advanced Controllers for Power Systems and Electronics: Wind Turbine as a Case Study Deep Deterministic Policy Gradient-based Parameter Selection Method of Notch Filters for Suppressing Mechanical Resonance in Industrial Servo Systems A General Real-time OPF Algorithm Using DDPG with Multiple Simulation Platforms Study on deep reinforcement learning techniques for building energy consumption forecasting Dynamic Experience Replay Deep Deterministic Policy Gradient (DDPG)-Based Resource Allocation Scheme for NOMA Vehicular Communications QoE-Based Cooperative Task Offloading with Deep Reinforcement Learning in Mobile Edge Networks Hybrid Policy Gradient for Deep Reinforcement Learning A New Adaptive Type-II Fuzzy-Based Deep Reinforcement Learning Control: Fuel Cell Air-Feed Sensors Control SLAs-Aware Online Task Scheduling Based on Deep Reinforcement Learning Method in Cloud Environment Shapley Q-Value: A Local Reward Approach to Solve Global Reward Games Distributed Prioritized Experience Replay Learning-based Delay Optimization for Self-Backhauled Millimeter Wave Cellular Networks On-Line Building Energy Optimization Using Deep Reinforcement Learning Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning Deep Deterministic Policy Gradient for Navigation of Mobile Robots in Simulated Environments Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning Deep Multi-agent Reinforcement Learning in a Common-Pool Resource System Stock Trading Bot Using Deep Reinforcement Learning Optimal Electric Vehicle Charging Strategy With Markov Decision Process and Reinforcement Learning Technique Black or White? How to Develop an AutoTuner for Memory-based Analytics Distributed Deep Deterministic Policy Gradient for Power Allocation Control in D2D-Based V2V Communications Distributed Prioritized Experience Replay Multi-Agent Reinforcement Learning for Multi-Object Tracking Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs Customised pearlmutter propagation: A hardware architecture for trust region policy optimisation Stable Policy Optimization via Off-Policy Divergence Regularization Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic Hindsight Trust Region Policy Optimization Multi-task Learning with Gradient Guided Policy Specialization Multi-rotor Robot Learning to Fly in a Bio-inspired Way Using Reinforcement Learning Optimization of TRPO process parameters for americium extraction from high level waste Adaptive Trust Region Policy Optimization: Global Convergence and Faster Rates for Regularized MDPs Count-Based Exploration in Feature Space for Reinforcement Learning Trust region based stochastic optimization with adaptive restart: A family of global optimization algorithms Trajectory piecewise quadratic reduced-order model for subsurface flow, with application to PDE-constrained optimization Wasserstein Reinforcement Learning Mid-Range Approximations in Sub-Spaces for MDO Problems with Disparate Discipline Attributes Count-Based Exploration in Feature Space for Reinforcement Learning Mirror Descent Policy Optimization
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