In this paper , we propose an approach which uses a mix of Evolutionary Strategies and Reinforcement Learning ( RL ) tech- niques to find an approximation of the optimal solution . The RL based agent uses the Policy Gradient method , an evolutionary reward strategy and a Pool of good ( not-optimal ) solutions to find the approximation . We find that the RL agent learns near-optimal solutions that outperforms the heuristic solutions . In addition , since state and action spaces of controlled systems are very large in real problems such as robot control , the learning of the value function is also difficult . In this study , we propose a learning scheme for a CPG controller called a CPG-actor-critic model , whose learning algorithm is based on a policy gradient method . We apply our RL method to autonomous acquisition of biped locomotion by a biped robot simulator . Specifically , we use a serialization-and-backtracking method to effectively deal with large discrete action space . We also adopt a policy gradient based method to improve the training efficiency and convergence to optimality . Extensive experimental results demonstrate that NFVdeep converges fast in the training process and responds rapidly to arriving requests especially in large , frequently transferred network state space . On the other hand , kMPs are known to capture the underlying structure of walking and yield a set of derived behaviors . We first generate walking gaits from D-RL , which uses policy gradient based approaches . We then analyze the resulting walking by using principal component analysis . Previous RL - based KBC methods typically use REINFORCE to learn the policy . However , policy gradient methods generally suffer from low sample efficiency , especially when the reward signal is sparse , because large numbers of Monte Carlo rollouts are usually needed to obtain many trajectories with positive terminal reward , particularly in the early stages of learning . To address this challenge , we develop a novel RL algorithm that uses MCTS to exploit the deterministic MDP transition defined in ( [ reference ] ) . The task consists of choosing actions in an Atari emulator based on raw images of the screen . Previous work has tackled this task using Q - learning with epsilon - greedy exploration , as well as Monte Carlo tree search and policy gradient methods . We use Deep Q Networks ( DQN ) as the reinforcement learning algorithm within our method , and compare its performance to the same DQN method using only epsilon - greedy exploration , Boltzman exploration , and a Thompson sampling approach . However , opponents argue that because it contains no qualitative properties , PPO lacks the resources to individuate powers , and generates a regress . The challenge for those who take such arguments seriously is to introduce qualitative properties without reintroducing the problems that PPO was meant to solve . In this paper , I distinguish the core claim of PPO : ( i ) basic physical properties have dispositional essences , from a hitherto unnoticed assumption : ( ii ) the dispositional essences of basic physical properties exclusively involve type-causal relations to other basic physical properties . Additionaly , we introduce Long Short-term Memory ( LSTM ) to represent the actor and critic , and buffers to store the cell state and hidden state of LSTM for the initialization of each episode , which helps solve the problem of inaccurate inital LSTM states . Distributed PPO with fully-connected neural networks and LSTM is utilized to train the manipulator and mobile robot with an exponential reward function to track the given trajectories . Numerical simulations are provided to demonstrate the effectiveness of the proposed method . To solve this problem , we propose a new federated reinforcement learning architecture where each agent working on its independent device shares their learning experience with each other , and transfers a mature policy model parameters into other agents . We incorporate the Actor-Critic PPO algorithm into each agent in the proposed collaborative architecture , and propose an efficient procedure for the gradient sharing and the model transfer . We also use edge computing to solve network problems that occur when training multiple real devices at the same time . The global search technique has been hybridized with binary successive approximation (BSA)-based evolutionary search method for exploring the search space locally . The relative performance of PPO and hybrid PPO has been evaluated by applying these techniques to standard mathematical test functions . The above-proposed hybrid search technique has been applied to achieve the solution for multi-parameter and multi-objective optimization problem of low-pass ( LP ) , high-pass ( HP ) , band-pass ( BP ) and band-stop ( BS ) digital IIR filter design . Moreover , among workers with a choice of plans , relatively few ( 19 % ) choose CDHPs when offered another type of plan option , such as preferred provider organizations ( PPOs ) or health maintenance organizations ( HMO ) . Comparable take-up rates for PPO and HMO plans when employees have a choice of another plan type were 55 percent and 40 percent , respectively . The modeling method to support product development processes ( PDP ) must have certain characteristics including the ability to represent multiple viewpoints of the product development and integrate with currently available analysis and design methods based on CE concept . This paper describes the reference model to support multiple viewpoints ( PPO : Products , Processes , and Organizations/Resources viewpoints ) of the product development processes , from which each model ( Products model , Processes model , and Organizations/Resources model ) can be extracted , as well as produces PPO data schema . This reference model has associative relationships among the products , processes , and organizations/resources . We present the first Reinforcement Learning gym framework that emulates this collective behavior of neurons and allows us to find suppression parameters for the environment of synthetic degenerate models of neurons . We successfully suppress synchrony via RL for three pathological signaling regimes , characterize the framework 's stability to noise , and further remove the unwanted oscillations by engaging multiple PPO agents . Thus , each agent not only receives low-level details but also considers coordination from higher levels , thereby obtaining global information to improve the training performance . Then , we test the proposed RLHC algorithm against the benchmark algorithm , proximal policy optimisation ( PPO ) , for two experimental scenarios performed in a Unity environment consisting of tennis and soccer agents ' competitions . The results showed that RLHC outperforms the benchmark on both competition tasks . The results obtained from the proposed technique are compared with the existing technique . From the numerical results , it is experienced that the PPO based approach is able to provide a better solution . We proposed a dynamic point of preferred operation ( PPO ) as a reference value to flatten the fluctuation of the total electricity load . We investigate the total electricity load for uncontrolled charging , static PPO and the proposed dynamic PPO schemes for charging-only , discharging , and bidirectional energy flow scenarios . Results show that the proposed dynamic PPO scheme can significantly shave the peak and alleviate the fluctuation of the total electricity load in a residential area . In contrast to the previously discussed value - based methods , A3C is an on - policy algorithm , and does not use a GPU nor a replay buffer . Proximal Policy Optimization ( PPO ) is a closely related actor - critic algorithm schulman2017proximal , which replaces the advantage pgadv with , where is as defined in Section [ reference ] . Although both PPO and A3C run parallel workers collecting trajectories independently in the environment , PPO collects these experiences to perform a single , synchronous , update in contrast with the asynchronous updates of A3C. Actor - Critic Experience Replay ( ACER ) extends the A3C framework with an experience replay buffer , Retrace algorithm for off - policy corrections , and the Truncated Importance Sampling Likelihood Ratio ( TISLR ) algorithm used for off - policy policy optimization wang2017sample . GPU Advantage Actor Critic ga3c , Distributed PPO heess : dppo and AlphaGo alphago . Deep learning frameworks such as TensorFlow tensorflow support distributed training , making large scale machine learning systems easier to implement and deploy . The control objective is to achieve $ L^2 $ norm regulation of the traffic state to a spatially uniform density and velocity . A recently developed neural network based policy gradient algorithm is employed , called proximal policy optimization . For comparison , we also consider an open-loop controller and a PDE backstepping approach . We also give a theoretical guarantee of the stable performance . We build our scheme upon two widely-adopted on-policy methods , the Trust-Region Policy Optimization ( TRPO ) algorithm and Proximal Policy Optimization ( PPO ) algorithm . We conduct extensive experiments on several MuJoCo tasks and their sparsified variants to fairly evaluate the proposed method . To overcome the complexity problems of the classic partially observable Markov decision processes ( POMDPs ) based dialog manager , we use a frame-based belief state representation that reduces the complexity of belief update . We also used dialog examples to maintain a reasonable number of system actions to reduce the complexity of the optimizing policy . We developed weather information and car navigation dialog system that employed a frame-based probabilistic framework . And then an approach combining with Cyclic Critical Path and Reverse Critical Path is present . An optimizing policy based on work-hour and workload is adopted in this paper in order to complete all tasks within project duration . We propose a general framework for entropy-regularized average-reward reinforcement learning in Markov decision processes ( MDPs ) . Our approach is based on extending the linear-programming formulation of policy optimization in MDPs to accommodate convex regularization functions . Our key result is showing that using the conditional entropy of the joint state-action distributions as regularization yields a dual optimization problem closely resembling the Bellman optimality equations . In particular , it remains elusive how to design a provably efficient policy optimization algorithm that incorporates exploration . To bridge such a gap , this paper proposes an Optimistic variant of the Proximal Policy Optimization algorithm ( OPPO ) , which follows an " optimistic version " of the policy gradient direction . This paper proves that , in the problem of episodic Markov decision process with linear function approximation , unknown transition , and adversarial reward with full-information feedback , OPPO achieves $ \tilde{O}(\sqrt{d^3 H^3 T})$ regret . On artificial tasks with delayed rewards , RUDDER is significantly faster than MC and exponentially faster than Monte Carlo Tree Search ( MCTS ) , TD({\lambda } ) , and reward shaping approaches . At Atari games , RUDDER on top of a Proximal Policy Optimization ( PPO ) baseline improves the scores , which is most prominent at games with delayed rewards . Source code is available at \url{this https URL } and demonstration videos at \url{this https URL}. Action selection is guided by a given reference image , which the agent attempts to replicate subject to the limitations of the action space and the agent 's learned policy . The painting agent policy is determined using a variant of proximal policy optimization reinforcement learning . During training , our agent is presented with patches sampled from an ensemble of reference images . In M - Walk , it is used as a prior to bias the MCTS search . And defines the long - term reward of taking action at state and then following the optimal policy thereafter . The objective is to learn a policy that maximizes the terminal rewards , i.e. , correctly identifies the target node with high probability . Ω to update M. update σ . optimized policy π In contrast to the previously discussed value - based methods , A3C is an on - policy algorithm , and does not use a GPU nor a replay buffer . Proximal Policy Optimization ( PPO ) is a closely related actor - critic algorithm schulman2017proximal , which replaces the advantage pgadv with , where is as defined in Section [ reference ] . Although both PPO and A3C run parallel workers collecting trajectories independently in the environment , PPO collects these experiences to perform a single , synchronous , update in contrast with the asynchronous updates of A3C. Actor - Critic Experience Replay ( ACER ) extends the A3C framework with an experience replay buffer , Retrace algorithm for off - policy corrections , and the Truncated Importance Sampling Likelihood Ratio ( TISLR ) algorithm used for off - policy policy optimization wang2017sample . We compare SDPG with the state-of-art policy gradient method in DRL , distributed distributional deterministic policy gradients ( D4PG ) , which has demonstrated state-of-art performance . We apply SDPG and D4PG to multiple OpenAI Gym environments and observe that our algorithm shows better sample efficiency as well as higher reward for most tasks . Experimentally we examine the contribution of each of these individual components , and show how they interact , as well as their combined contributions . Our results show that across a wide variety of simple control tasks , difficult manipulation tasks , and a set of hard obstacle-based locomotion tasks the D4PG algorithm achieves state of the art performance . The same models can be effortlessly deployed to different cluster architectures ( i.e. one or many machines containing CPUs , GPUs or TPU accelerators ) using synchronous or asynchronous training regimes . To demonstrate the generality and scalability of TF-Replicator , we implement and benchmark three very different models : ( 1 ) A ResNet-50 for ImageNet classification , ( 2 ) a SN-GAN for class-conditional ImageNet image generation , and ( 3 ) a D4PG reinforcement learning agent for continuous control . Our results show strong scalability performance without demanding any distributed systems expertise of the user . An open source simulation environment GymFC is used for training of quad-copter . The results for comparative analysis of DDPG & D4PG algorithms are also presented , highlighting the attitude control performance . Subsequently , hessel2018rainbow combined C51 with enhancements such as prioritized experience replay , - step updates , and the dueling architecture , leading to the Rainbow agent , current state - of - the - art in Atari - 57 . The categorical parameterization , using the projected KL loss , has also been used in recent work to improve the critic of a policy gradient algorithm , D4PG , achieving significantly improved robustness and state - of - the - art performance across a variety of continuous control tasks . subsection : - Wasserstein Metric Next , we propose a distributed framework of DDPG , where the synchronous workers generate transitions and compute gradients for the global network and the collecting workers only produce transitions for the shared replay memory with different policies and exploration noises . We use this proposed distributed DDPG with prioritized experience replay to train the SCARA robot and mobile robot to track the same trajectories , which presents a faster learning speed and smaller tracking errors compared with the single-worker DDPG . Next , we study on the proximal policy optimization algorithm ( PPO ) with generalized advantage estimation ( GAE ) . Numerical simulations are provided to demonstrate the effectiveness and efficiency of the proposed methods . It can be seen from the simulation results that the agent trained by the proposed distributed DDPG could learn faster and achieve smaller tracking errors than DDPG .
In this paper , we propose an approach which uses a mix of Evolutionary Strategies and Reinforcement Learning ( RL ) tech- niques to find an approximation of the optimal solution . The RL based agent uses the Policy Gradient method , an evolutionary reward strategy and a Pool of good ( not-optimal ) solutions to find the approximation . We find that the RL agent learns near-optimal solutions that outperforms the heuristic solutions .
In addition , since state and action spaces of controlled systems are very large in real problems such as robot control , the learning of the value function is also difficult . In this study , we propose a learning scheme for a CPG controller called a CPG-actor-critic model , whose learning algorithm is based on a policy gradient method . We apply our RL method to autonomous acquisition of biped locomotion by a biped robot simulator .
Specifically , we use a serialization-and-backtracking method to effectively deal with large discrete action space . We also adopt a policy gradient based method to improve the training efficiency and convergence to optimality . Extensive experimental results demonstrate that NFVdeep converges fast in the training process and responds rapidly to arriving requests especially in large , frequently transferred network state space .
On the other hand , kMPs are known to capture the underlying structure of walking and yield a set of derived behaviors . We first generate walking gaits from D-RL , which uses policy gradient based approaches . We then analyze the resulting walking by using principal component analysis .
Previous RL - based KBC methods typically use REINFORCE to learn the policy . However , policy gradient methods generally suffer from low sample efficiency , especially when the reward signal is sparse , because large numbers of Monte Carlo rollouts are usually needed to obtain many trajectories with positive terminal reward , particularly in the early stages of learning . To address this challenge , we develop a novel RL algorithm that uses MCTS to exploit the deterministic MDP transition defined in ( [ reference ] ) .
The task consists of choosing actions in an Atari emulator based on raw images of the screen . Previous work has tackled this task using Q - learning with epsilon - greedy exploration , as well as Monte Carlo tree search and policy gradient methods . We use Deep Q Networks ( DQN ) as the reinforcement learning algorithm within our method , and compare its performance to the same DQN method using only epsilon - greedy exploration , Boltzman exploration , and a Thompson sampling approach .
However , opponents argue that because it contains no qualitative properties , PPO lacks the resources to individuate powers , and generates a regress . The challenge for those who take such arguments seriously is to introduce qualitative properties without reintroducing the problems that PPO was meant to solve . In this paper , I distinguish the core claim of PPO : ( i ) basic physical properties have dispositional essences , from a hitherto unnoticed assumption : ( ii ) the dispositional essences of basic physical properties exclusively involve type-causal relations to other basic physical properties .
Additionaly , we introduce Long Short-term Memory ( LSTM ) to represent the actor and critic , and buffers to store the cell state and hidden state of LSTM for the initialization of each episode , which helps solve the problem of inaccurate inital LSTM states . Distributed PPO with fully-connected neural networks and LSTM is utilized to train the manipulator and mobile robot with an exponential reward function to track the given trajectories . Numerical simulations are provided to demonstrate the effectiveness of the proposed method .
To solve this problem , we propose a new federated reinforcement learning architecture where each agent working on its independent device shares their learning experience with each other , and transfers a mature policy model parameters into other agents . We incorporate the Actor-Critic PPO algorithm into each agent in the proposed collaborative architecture , and propose an efficient procedure for the gradient sharing and the model transfer . We also use edge computing to solve network problems that occur when training multiple real devices at the same time .
The global search technique has been hybridized with binary successive approximation (BSA)-based evolutionary search method for exploring the search space locally . The relative performance of PPO and hybrid PPO has been evaluated by applying these techniques to standard mathematical test functions . The above-proposed hybrid search technique has been applied to achieve the solution for multi-parameter and multi-objective optimization problem of low-pass ( LP ) , high-pass ( HP ) , band-pass ( BP ) and band-stop ( BS ) digital IIR filter design .
Moreover , among workers with a choice of plans , relatively few ( 19 % ) choose CDHPs when offered another type of plan option , such as preferred provider organizations ( PPOs ) or health maintenance organizations ( HMO ) . Comparable take-up rates for PPO and HMO plans when employees have a choice of another plan type were 55 percent and 40 percent , respectively .
The modeling method to support product development processes ( PDP ) must have certain characteristics including the ability to represent multiple viewpoints of the product development and integrate with currently available analysis and design methods based on CE concept . This paper describes the reference model to support multiple viewpoints ( PPO : Products , Processes , and Organizations/Resources viewpoints ) of the product development processes , from which each model ( Products model , Processes model , and Organizations/Resources model ) can be extracted , as well as produces PPO data schema . This reference model has associative relationships among the products , processes , and organizations/resources .
We present the first Reinforcement Learning gym framework that emulates this collective behavior of neurons and allows us to find suppression parameters for the environment of synthetic degenerate models of neurons . We successfully suppress synchrony via RL for three pathological signaling regimes , characterize the framework 's stability to noise , and further remove the unwanted oscillations by engaging multiple PPO agents .
Thus , each agent not only receives low-level details but also considers coordination from higher levels , thereby obtaining global information to improve the training performance . Then , we test the proposed RLHC algorithm against the benchmark algorithm , proximal policy optimisation ( PPO ) , for two experimental scenarios performed in a Unity environment consisting of tennis and soccer agents ' competitions . The results showed that RLHC outperforms the benchmark on both competition tasks .
The results obtained from the proposed technique are compared with the existing technique . From the numerical results , it is experienced that the PPO based approach is able to provide a better solution .
We proposed a dynamic point of preferred operation ( PPO ) as a reference value to flatten the fluctuation of the total electricity load . We investigate the total electricity load for uncontrolled charging , static PPO and the proposed dynamic PPO schemes for charging-only , discharging , and bidirectional energy flow scenarios . Results show that the proposed dynamic PPO scheme can significantly shave the peak and alleviate the fluctuation of the total electricity load in a residential area .
In contrast to the previously discussed value - based methods , A3C is an on - policy algorithm , and does not use a GPU nor a replay buffer . Proximal Policy Optimization ( PPO ) is a closely related actor - critic algorithm schulman2017proximal , which replaces the advantage pgadv with , where is as defined in Section [ reference ] . Although both PPO and A3C run parallel workers collecting trajectories independently in the environment , PPO collects these experiences to perform a single , synchronous , update in contrast with the asynchronous updates of A3C. Actor - Critic Experience Replay ( ACER ) extends the A3C framework with an experience replay buffer , Retrace algorithm for off - policy corrections , and the Truncated Importance Sampling Likelihood Ratio ( TISLR ) algorithm used for off - policy policy optimization wang2017sample .
GPU Advantage Actor Critic ga3c , Distributed PPO heess : dppo and AlphaGo alphago . Deep learning frameworks such as TensorFlow tensorflow support distributed training , making large scale machine learning systems easier to implement and deploy .
The control objective is to achieve $ L^2 $ norm regulation of the traffic state to a spatially uniform density and velocity . A recently developed neural network based policy gradient algorithm is employed , called proximal policy optimization . For comparison , we also consider an open-loop controller and a PDE backstepping approach .
We also give a theoretical guarantee of the stable performance . We build our scheme upon two widely-adopted on-policy methods , the Trust-Region Policy Optimization ( TRPO ) algorithm and Proximal Policy Optimization ( PPO ) algorithm . We conduct extensive experiments on several MuJoCo tasks and their sparsified variants to fairly evaluate the proposed method .
To overcome the complexity problems of the classic partially observable Markov decision processes ( POMDPs ) based dialog manager , we use a frame-based belief state representation that reduces the complexity of belief update . We also used dialog examples to maintain a reasonable number of system actions to reduce the complexity of the optimizing policy . We developed weather information and car navigation dialog system that employed a frame-based probabilistic framework .
And then an approach combining with Cyclic Critical Path and Reverse Critical Path is present . An optimizing policy based on work-hour and workload is adopted in this paper in order to complete all tasks within project duration .
We propose a general framework for entropy-regularized average-reward reinforcement learning in Markov decision processes ( MDPs ) . Our approach is based on extending the linear-programming formulation of policy optimization in MDPs to accommodate convex regularization functions . Our key result is showing that using the conditional entropy of the joint state-action distributions as regularization yields a dual optimization problem closely resembling the Bellman optimality equations .
In particular , it remains elusive how to design a provably efficient policy optimization algorithm that incorporates exploration . To bridge such a gap , this paper proposes an Optimistic variant of the Proximal Policy Optimization algorithm ( OPPO ) , which follows an " optimistic version " of the policy gradient direction . This paper proves that , in the problem of episodic Markov decision process with linear function approximation , unknown transition , and adversarial reward with full-information feedback , OPPO achieves $ \tilde{O}(\sqrt{d^3 H^3 T})$ regret .
On artificial tasks with delayed rewards , RUDDER is significantly faster than MC and exponentially faster than Monte Carlo Tree Search ( MCTS ) , TD({\lambda } ) , and reward shaping approaches . At Atari games , RUDDER on top of a Proximal Policy Optimization ( PPO ) baseline improves the scores , which is most prominent at games with delayed rewards . Source code is available at \url{this https URL } and demonstration videos at \url{this https URL}.
Action selection is guided by a given reference image , which the agent attempts to replicate subject to the limitations of the action space and the agent 's learned policy . The painting agent policy is determined using a variant of proximal policy optimization reinforcement learning . During training , our agent is presented with patches sampled from an ensemble of reference images .
In M - Walk , it is used as a prior to bias the MCTS search . And defines the long - term reward of taking action at state and then following the optimal policy thereafter . The objective is to learn a policy that maximizes the terminal rewards , i.e. , correctly identifies the target node with high probability .
Ω to update M. update σ . optimized policy π
In contrast to the previously discussed value - based methods , A3C is an on - policy algorithm , and does not use a GPU nor a replay buffer . Proximal Policy Optimization ( PPO ) is a closely related actor - critic algorithm schulman2017proximal , which replaces the advantage pgadv with , where is as defined in Section [ reference ] . Although both PPO and A3C run parallel workers collecting trajectories independently in the environment , PPO collects these experiences to perform a single , synchronous , update in contrast with the asynchronous updates of A3C. Actor - Critic Experience Replay ( ACER ) extends the A3C framework with an experience replay buffer , Retrace algorithm for off - policy corrections , and the Truncated Importance Sampling Likelihood Ratio ( TISLR ) algorithm used for off - policy policy optimization wang2017sample .
We compare SDPG with the state-of-art policy gradient method in DRL , distributed distributional deterministic policy gradients ( D4PG ) , which has demonstrated state-of-art performance . We apply SDPG and D4PG to multiple OpenAI Gym environments and observe that our algorithm shows better sample efficiency as well as higher reward for most tasks .
Experimentally we examine the contribution of each of these individual components , and show how they interact , as well as their combined contributions . Our results show that across a wide variety of simple control tasks , difficult manipulation tasks , and a set of hard obstacle-based locomotion tasks the D4PG algorithm achieves state of the art performance .
The same models can be effortlessly deployed to different cluster architectures ( i.e. one or many machines containing CPUs , GPUs or TPU accelerators ) using synchronous or asynchronous training regimes . To demonstrate the generality and scalability of TF-Replicator , we implement and benchmark three very different models : ( 1 ) A ResNet-50 for ImageNet classification , ( 2 ) a SN-GAN for class-conditional ImageNet image generation , and ( 3 ) a D4PG reinforcement learning agent for continuous control . Our results show strong scalability performance without demanding any distributed systems expertise of the user .
An open source simulation environment GymFC is used for training of quad-copter . The results for comparative analysis of DDPG & D4PG algorithms are also presented , highlighting the attitude control performance .
Subsequently , hessel2018rainbow combined C51 with enhancements such as prioritized experience replay , - step updates , and the dueling architecture , leading to the Rainbow agent , current state - of - the - art in Atari - 57 . The categorical parameterization , using the projected KL loss , has also been used in recent work to improve the critic of a policy gradient algorithm , D4PG , achieving significantly improved robustness and state - of - the - art performance across a variety of continuous control tasks . subsection : - Wasserstein Metric
Next , we propose a distributed framework of DDPG , where the synchronous workers generate transitions and compute gradients for the global network and the collecting workers only produce transitions for the shared replay memory with different policies and exploration noises . We use this proposed distributed DDPG with prioritized experience replay to train the SCARA robot and mobile robot to track the same trajectories , which presents a faster learning speed and smaller tracking errors compared with the single-worker DDPG . Next , we study on the proximal policy optimization algorithm ( PPO ) with generalized advantage estimation ( GAE ) .
Numerical simulations are provided to demonstrate the effectiveness and efficiency of the proposed methods . It can be seen from the simulation results that the agent trained by the proposed distributed DDPG could learn faster and achieve smaller tracking errors than DDPG .
Evolutionary RL for Container Loading Reinforcement Learning for CPG-Driven Biped Robot NFVdeep: Adaptive Online Service Function Chain Deployment with Deep Reinforcement Learning Realizing Learned Quadruped Locomotion Behaviors through Kinematic Motion Primitives M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models Inverse functionalism and the individuation of powers Trajectory-Tracking Control of Robotic System via Proximal Policy Optimization Federated Reinforcement Learning for Controlling Multiple Rotary Inverted Pendulums in Edge Computing Environments Design of Digital IIR Filter with Conflicting Objectives Using Hybrid Predator–Prey Optimization Behind the slow enrollment growth of employer-based consumer-directed health plans. A Semantic Network Approach to PPO (Products, Processes, Organizations/Resources) Modeling for PDM Systems Reinforcement Learning Framework for Deep Brain Stimulation Study Reinforcement Learning from Hierarchical Critics Scheduling short-term hydrothermal generation using predator prey optimization technique A dynamic point of preferred operation (PPO) scheme for charging electric vehicles in a residential area The Reactor: A Sample-Efficient Actor-Critic Architecture Distributed Prioritized Experience Replay Reinforcement Learning vs. Backstepping Control of Stop-and-Go Traffic Multi-Path Policy Optimization A Frame-Based Probabilistic Framework for Spoken Dialog Management Using Dialog Examples A case of ship design tasks assignment and optimization A unified view of entropy-regularized Markov decision processes Provably Efficient Exploration in Policy Optimization RUDDER: Return Decomposition for Delayed Rewards PaintBot: A Reinforcement Learning Approach for Natural Media Painting M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models The Reactor: A Sample-Efficient Actor-Critic Architecture Sample-based Distributional Policy Gradient Distributed Distributional Deterministic Policy Gradients TF-Replicator: Distributed Machine Learning for Researchers Attitude Control of Quad-copter using Deterministic Policy Gradient Algorithms (DPGA) Implicit Quantile Networks for Distributional Reinforcement Learning Continuous control for robot based on deep reinforcement learning Trajectory-Tracking Control of Robotic Systems via Deep Reinforcement Learning
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