Prior to any interaction with the environment , a Gnu-RL agent is pre-trained on historical data using imitation learning , which enables it to match the behavior of the existing controller . Once it is put in charge of controlling the environment , the agent continues to improve its policy end-to-end , using a policy gradient algorithm . We evaluate Gnu-RL on both an EnergyPlus model and a real-world testbed . We evaluate our framework for learning problems with model uncertainty . We measure and control for \emph{epistemic } risk using dynamic programming ( DP ) and policy gradient-based algorithms . The risk-averse behavior is then compared with the behavior of the optimal risk-neutral policy in environments with epistemic risk . In this chapter , we will cover the basics of the policy-based approaches especially the policy gradient-based approaches . We will understand why policy-based approaches are superior to that of value-based approaches under some circumstances and why they are also tough to implement . document : SeqGAN : Sequence Generative Adversarial Nets with Policy Gradient As a new way of training generative models , Generative Adversarial Net ( GAN ) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real - valued data . In recent RL success stories ( e.g. , super-human performance on video games [ Mnih et al. , 2015 ] ) , we noticethat most of them rely on random exploration strategies , such as ? -greedy . Similarly , policy gradient method such as REINFORCE [ Williams , 1992 ] , perform exploration by injecting randomness into action space and hope the randomness can lead to a good sequence of actions that achieves high total reward . The theoretical RL literature has developed more sophisticated algorithms for efficient exploration ( e.g. , [ Azar et al. , 2017 ] ) , however , the samplecomplexity of these near-optimal algorithms has to scale exponentially with respect to key parameters of underlying systems such as dimensions of state and action space . Exploiting variable augmentation , REINFORCE , and reparameterization , the ARM estimator achieves adaptive variance reduction for Monte Carlo integration by merging two expectations via common random numbers . The variance-reduction mechanism of the ARM estimator can also be attributed to either antithetic sampling in an augmented space , or the use of an optimal anti-symmetric " self-control " baseline function together with the REINFORCE estimator in that augmented space . Experimental results show the ARM estimator provides state-of-the-art performance in auto-encoding variational inference and maximum likelihood estimation , for discrete latent variable models with one or multiple stochastic binary layers . Gradient based methods are believed to be more advantageous than value-function based methods in huge state spaces and in POMDP settings . Probably the ﬁrst gradientbased RL formulation is class of REINFORCE algorithms ofWilliams ( ? ) . Other more recent methods are , e.g. , ( ? ; ? ; ? ) . SCST is a form of the popular REINFORCE algorithm that , rather than estimating a baseline to normalize the rewards and reduce variance , utilizes the output of its own test-time inference algorithm to normalize the rewards it experiences . Using this approach , estimating the reward signal ( as actor-critic methods must do ) and estimating normalization ( as REINFORCE algorithms typically do ) is avoided , while at the same time harmonizing the model with respect to its test-time inference procedure . Empirically we find that directly optimizing the CIDEr metric with SCST and greedy decoding at test-time is highly effective . In this paper we instead apply reinforcement learning to directly optimize a neural mention-ranking model for coreference evaluation metrics . We experiment with two approaches : the REINFORCE policy gradient algorithm and a reward-rescaled max-margin objective . We find the latter to be more effective , resulting in significant improvements over the current state-of-the-art on the English and Chinese portions of the CoNLL 2012 Shared Task . This separation presents two-fold benefits i ) min- imize the path management complexity at the SDN controller ii ) orders of magnitude reduction in the switch Ternary Content Addressable Memory ( TCAM ) rules ; thus it enables for scalable , agile and flexible service function chaining . Finally , in order to achieve efficient NF migration and to address HA for NF chains , we present REINFORCE - an integrated framework to address failure resiliency for individual NF failures and global service chain-wide failures . REINFORCE presents a novel NF state replication strategy and distinct mechanisms to provide timely detection of NFs , hardware node ( Virtualized Network Function Manager ) , and net- work link failures ; and provides distinct failover mechanisms with strict correctness guarantees . Regarding state dimension reduction : we evaluates how these state representation methods capture relevant task information and how much compactness should a state representation be . Regarding controllability : experiments are designed to use different state representation methods in a traditional visual servoing controller and a REINFORCE controller . We analyze the challenges arisen from the representation itself other than from control algorithms . We design a combination mechanism that allows the two modules to learn simultaneously by passing the estimated value between the two modules while interacting with the environment . We use REINFORCE + DPG to solve the problem of continuous motion parameter space , and use REINFORCE + Q learning to solve the problem of discrete motion parameter space . To testing the algorithm model , we designed and realized an aircraft bombing simulation environment . The RAM thus consists of three components : a glimpse sub-network analyzing the quality at a fixation using multi-scale information , a location subnetwork selecting where to look next by sampling a stochastic node , and a recurrent network aggregating information along the scanning path to compute the final prediction . The RAM is formulated under multi-task learning for the joint prediction of distortion type and image quality score and for the REINFORCE rule [ 21 ] used to handle the stochastic node . The RAM is trained through back-propagation . As policy gradient method is a kind of generalized policy iteration , we show how these differences in policy iteration are reflected in policy gradient for AMGs . We formulate an adversarial policy gradient and discuss potential possibilities for developing better policy gradient methods other than self-play REINFORCE . The core idea is to estimate the minimum rather than the mean for the “ critic ” . SVPG is robust to initialization and can easily be implemented in a parallel manner . On continuous control problems , we find that implementing SVPG on top of REINFORCE and advantage actor-critic algorithms improves both average return and data efficiency . 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 . Intuitively , the loss function is to force the goal vector to match the transition in the feature space while achieving high reward . At the same time , the WORKER is trained to maximize the reward using the REINFORCE algorithm [ reference ] as is done in [ reference ] , [ reference ] We use gt ( θm ) to explicitly show gt is parameterized by θm . which can be approximated by sampling the state s t−1 and the action x t taken by WORKER . [ reference ] . The gradient with respect to the non - differentiable term can be estimated with REINFORCE williams1992simple , while the term can be trained using ordinary backpropagation . section : Experiments However , at test time , these models do not produce a single token but a whole sequence . In order to resolve this inconsistency and to potentially improve generation , recent work has focused on training these models at the sequence - level , for instance using REINFORCE ranzato2015sequence , actor - critic bahdanau2016ac , or with beam search optimization wiseman2016acl . Before the recent work on sequence level training for neural networks , there has been a large body of research on training linear models at the sequence level . Our proposed methodology borrows ideas from the reinforcement learning literature sutton - rl . In particular , we build on the REINFORCE algorithm proposed by reinforce , to achieve the above two objectives . While sampling from the model during training is quite a natural step for the REINFORCE algorithm , optimizing directly for any test metric can also be achieved by it . Moreover , MIXER is several times faster since it relies only on greedy search . It is worth mentioning that the REINFORCE baseline did not work for these applications . Exploration from a random policy has little chance of success . This gives rise to the challenging problem of determining the target for the next network output . bengio2015scheduled use the token from the ground - truth answer as the target for the network at step , whereas ranzato2015sequence rely on the REINFORCE algorithm williams1992simple to decide whether or not the tokens from a sampled prediction lead to a high task - specific score , such as BLEU papineni2002bleu or ROUGE lin2003automatic . In this work , we propose and study an alternative procedure for training sequence prediction networks that aims to directly improve their test time metrics ( which are typically not the log - likelihood ) . The parameteric estimate is called the critic . The above formula is similar in spirit to the REINFORCE learning rule that ranzato2015sequence use in the same context : where the scalar is called baseline or control variate . The difference is that in REINFORCE the inner sum over all actions is replaced by its 1 - sample estimate , namely , where the log probability is introduced to correct for the sampling of . The motivation behind this approach is that using the ground - truth output should produce a better baseline that lowers the variance of REINFORCE , resulting in higher task - specific scores . We refer to this method as REINFORCE - critic . subsection : Spelling Correction For training the ASDN , since we apply the sampling strategy to convert the heatmap into a binary mask , which is not differentiable , we can not directly back - prop the gradients from the classification loss . Alternatively , we take the inspirations from the REINFORCE approach . We compute which binary masks lead to significant drops in Fast - RCNN classification scores . This allows the policy network to be indirectly improved through Q - learning over the off - policy trajectories . Our method is in sharp contrast to existing RL - based methods for KBC , which use a policy gradients ( REINFORCE ) method and usually require a large number of rollouts to obtain a trajectory with a positive reward , especially in the early stages of learning . Experimental results on several benchmarks , including a synthetic task and several real - world KBC tasks , show that our approach learns better policies than previous RL - based methods and traditional KBC methods . We ran the experiments three times and report the means and standard deviations ( except for PRA , TransE , and TransR on NELL995 , whose results are directly quoted from ) . On the Three Glass Puzzle task , M - Walk significantly outperforms the baseline : the best model of M - Walk achieves an accuracy of while the best REINFORCE method achieves ( see Appendix [ reference ] for more experiments with different settings on this task ) . For the two KBC tasks , we report their results in Tables [ reference ] - [ reference ] , where PG - Walk and Q - Walk are two methods we created just for the ablation study in the next section . It shows that M - Walk converges much faster than PG and achieves better results on this task . In Table [ reference ] , we report the test accuracy of M - Walk and vanilla policy gradient ( REINFORCE / PG ) with different beam search sizes and different MCTS rollouts during testing . The number of MCTS simulations for training M - Walk is fixed to be . Gradients also backpropagate through the gating network to its inputs . Our method differs here from [ reference ] who use boolean gates and a REINFORCE - style approach to train the gating network . section : ADDRESSING PERFORMANCE CHALLENGES As the target Q function is changing slowly , we combine the one-step estimate with zero-step estimate to avoid sharp changes . The experimental results illustrate that CPG outperforms TD3 and some other reinforcement learning methods on multiple MuJoCo benchmarks . In this current research , Twin-Delayed DDPG ( TD3 ) algorithm has been used to solve the most challenging virtual Artificial Intelligence application by training a 4-ant-legged robot as an Intelligent Agent to run across a field . Twin-Delayed DDPG ( TD3 ) is an incredibly smart AI model of a Deep Reinforcement Learning which combines the state-of-the-art methods in Artificial Intelligence . These includes Policy gradient , Actor-Critics , and continuous Double Deep Q-Learning . In this current research work , we applied a Twin- Delayed DDPG ( TD3 ) algorithm to solve the most challenging virtual Artificial Intelligence application by training a HalfCheetah robot as an Intelligent Agent to run across a field . Twin-Delayed DDPG ( TD3 ) is a recent breakthrough smart AI model of a Deep Reinforcement Learning which combines the state-of-the-art techniques in Artificial Intelligence , including continuous Double Deep Q-Learning , Policy Gradient and Actor-Critic . These Deep Reinforcement Learning approaches have the capabilities to train an Intelligent agent to interact with an environment with automatic feature engineering , that is , requiring minimal domain knowledge . Twin-Delayed Deep Deterministic Policy Gradient algorithm ( TD3 ) was built on the Deep Deterministic Policy Gradient algorithm ( DDPG ) . During the implementation of the TD3 model , we used a two- layer feedforward neural network of 400 and 300 hidden nodes respectively , with Rectified Linear Units ( ReLU ) as an activation function between each layer for both the Actor and Critics , and then a final tanh unit following the output of the Actor . The Critic network evaluates the confidence of the new bounding box online to determine whether the Critic model needs to be updated or re-initialized . In further , in our model we use TD3 algorithm to further optimize the AC model by using two Critic networks to jointly predict the bounding box confidence , and to obtain the smaller predicted value as the label to update the network parameters , thereby rendering the Critic network to avoid excessive estimation bias , accelerate the convergence of the loss function , and obtain more accurate prediction values . Also , a small amount of random noise with upper and lower bounds are added to the action in the Actor model , and the search area is reasonably expanded in offline learning to improve the robustness of the tracking method under strong background interference and fast object motion . MVPI enjoys great flexibility in that any policy evaluation method and risk-neutral control method can be dropped in for risk-averse control off the shelf , in both on- and off-policy settings . We propose risk-averse TD3 as an example instantiating MVPI , which outperforms vanilla TD3 and many previous risk-averse control methods in challenging Mujoco robot simulation tasks under a risk-aware performance metric . This risk-averse TD3 is the first to introduce deterministic policies and off-policy learning into risk-averse reinforcement learning , both of which are key to the performance boost we show in Mujoco domains . MVPI adopts a per-step reward perspective ( Bisi et al. , 2019 ) for risk-averse control , instead of the commonly used total reward perspective . In this study , image acquisition process through hardware-software are shown . Hardware acquisition of a 200 GB capacity hard disk is made through Tableau TD3 and CRU Ditto . The images of the same storage are taken through Tableau , CRU and RTX USB bridge and through FTK imager and Forensic Imager ; then comparative performance assessment results are presented . This work proposes the use of reinforcement learning ( RL ) techniques with deep-learning models to address the maximum power point tracking ( MPPT ) control problem of a photovoltaic ( PV ) array . We implemented the deep deterministic policy gradient ( DDPG ) method , the inverted gradient ( IGDDPG ) method and the delayed twins ( TD3 ) method to solve the MPPT control problem . Several simulation experiments were performed in the OpenAI Gym platform aiming to evaluate the performance of the proposed control strategies , under different operating conditions in terms of temperature and solar irradiance . Our mechanism builds on Double Q-learning , by mixing update action value based on the minimum and maximum between a pair of critics to limit the overestimation . We then propose a specific adaptation to the Twin Delayed Deep Deterministic policy gradient algorithm ( TD3 ) and show that the resulting algorithm not only reduces the observed overestimations , as hypothesized , but that this also leads to much better performance on several tasks . We further investigate the robustness of these tests to violations of the most common hypotheses ( normal distributions , same distributions , equal variances ) . Beside simulations , we compare empirical distributions obtained by running Soft-Actor Critic and Twin-Delayed Deep Deterministic Policy Gradient on Half-Cheetah . We conclude by providing guidelines and code to perform rigorous comparisons of RL algorithm performances . It is well known that Challenges such as background clutters , fast object motion and occlusion et al. affect a lot the robustness or accuracy of existing object tracking methods . This paper proposes a reinforcement learning model based on Twin Delayed Deep Deterministic algorithm ( TD3 ) for single object tracking . The model is based on the deep reinforcement learning model , Actor-Critic ( AC ) , in which the Actor network predicts a continuous action that moves the target bounding box in the previous frame to the object position in the current frame and adapts to the object size .
Prior to any interaction with the environment , a Gnu-RL agent is pre-trained on historical data using imitation learning , which enables it to match the behavior of the existing controller . Once it is put in charge of controlling the environment , the agent continues to improve its policy end-to-end , using a policy gradient algorithm . We evaluate Gnu-RL on both an EnergyPlus model and a real-world testbed .
We evaluate our framework for learning problems with model uncertainty . We measure and control for \emph{epistemic } risk using dynamic programming ( DP ) and policy gradient-based algorithms . The risk-averse behavior is then compared with the behavior of the optimal risk-neutral policy in environments with epistemic risk .
In this chapter , we will cover the basics of the policy-based approaches especially the policy gradient-based approaches . We will understand why policy-based approaches are superior to that of value-based approaches under some circumstances and why they are also tough to implement .
document : SeqGAN : Sequence Generative Adversarial Nets with Policy Gradient As a new way of training generative models , Generative Adversarial Net ( GAN ) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real - valued data .
In recent RL success stories ( e.g. , super-human performance on video games [ Mnih et al. , 2015 ] ) , we noticethat most of them rely on random exploration strategies , such as ? -greedy . Similarly , policy gradient method such as REINFORCE [ Williams , 1992 ] , perform exploration by injecting randomness into action space and hope the randomness can lead to a good sequence of actions that achieves high total reward . The theoretical RL literature has developed more sophisticated algorithms for efficient exploration ( e.g. , [ Azar et al. , 2017 ] ) , however , the samplecomplexity of these near-optimal algorithms has to scale exponentially with respect to key parameters of underlying systems such as dimensions of state and action space .
Exploiting variable augmentation , REINFORCE , and reparameterization , the ARM estimator achieves adaptive variance reduction for Monte Carlo integration by merging two expectations via common random numbers . The variance-reduction mechanism of the ARM estimator can also be attributed to either antithetic sampling in an augmented space , or the use of an optimal anti-symmetric " self-control " baseline function together with the REINFORCE estimator in that augmented space . Experimental results show the ARM estimator provides state-of-the-art performance in auto-encoding variational inference and maximum likelihood estimation , for discrete latent variable models with one or multiple stochastic binary layers .
Gradient based methods are believed to be more advantageous than value-function based methods in huge state spaces and in POMDP settings . Probably the ﬁrst gradientbased RL formulation is class of REINFORCE algorithms ofWilliams ( ? ) . Other more recent methods are , e.g. , ( ? ; ? ; ? ) .
SCST is a form of the popular REINFORCE algorithm that , rather than estimating a baseline to normalize the rewards and reduce variance , utilizes the output of its own test-time inference algorithm to normalize the rewards it experiences . Using this approach , estimating the reward signal ( as actor-critic methods must do ) and estimating normalization ( as REINFORCE algorithms typically do ) is avoided , while at the same time harmonizing the model with respect to its test-time inference procedure . Empirically we find that directly optimizing the CIDEr metric with SCST and greedy decoding at test-time is highly effective .
In this paper we instead apply reinforcement learning to directly optimize a neural mention-ranking model for coreference evaluation metrics . We experiment with two approaches : the REINFORCE policy gradient algorithm and a reward-rescaled max-margin objective . We find the latter to be more effective , resulting in significant improvements over the current state-of-the-art on the English and Chinese portions of the CoNLL 2012 Shared Task .
This separation presents two-fold benefits i ) min- imize the path management complexity at the SDN controller ii ) orders of magnitude reduction in the switch Ternary Content Addressable Memory ( TCAM ) rules ; thus it enables for scalable , agile and flexible service function chaining . Finally , in order to achieve efficient NF migration and to address HA for NF chains , we present REINFORCE - an integrated framework to address failure resiliency for individual NF failures and global service chain-wide failures . REINFORCE presents a novel NF state replication strategy and distinct mechanisms to provide timely detection of NFs , hardware node ( Virtualized Network Function Manager ) , and net- work link failures ; and provides distinct failover mechanisms with strict correctness guarantees .
Regarding state dimension reduction : we evaluates how these state representation methods capture relevant task information and how much compactness should a state representation be . Regarding controllability : experiments are designed to use different state representation methods in a traditional visual servoing controller and a REINFORCE controller . We analyze the challenges arisen from the representation itself other than from control algorithms .
We design a combination mechanism that allows the two modules to learn simultaneously by passing the estimated value between the two modules while interacting with the environment . We use REINFORCE + DPG to solve the problem of continuous motion parameter space , and use REINFORCE + Q learning to solve the problem of discrete motion parameter space . To testing the algorithm model , we designed and realized an aircraft bombing simulation environment .
The RAM thus consists of three components : a glimpse sub-network analyzing the quality at a fixation using multi-scale information , a location subnetwork selecting where to look next by sampling a stochastic node , and a recurrent network aggregating information along the scanning path to compute the final prediction . The RAM is formulated under multi-task learning for the joint prediction of distortion type and image quality score and for the REINFORCE rule [ 21 ] used to handle the stochastic node . The RAM is trained through back-propagation .
As policy gradient method is a kind of generalized policy iteration , we show how these differences in policy iteration are reflected in policy gradient for AMGs . We formulate an adversarial policy gradient and discuss potential possibilities for developing better policy gradient methods other than self-play REINFORCE . The core idea is to estimate the minimum rather than the mean for the “ critic ” .
SVPG is robust to initialization and can easily be implemented in a parallel manner . On continuous control problems , we find that implementing SVPG on top of REINFORCE and advantage actor-critic algorithms improves both average return and data efficiency .
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 .
Intuitively , the loss function is to force the goal vector to match the transition in the feature space while achieving high reward . At the same time , the WORKER is trained to maximize the reward using the REINFORCE algorithm [ reference ] as is done in [ reference ] , [ reference ] We use gt ( θm ) to explicitly show gt is parameterized by θm . which can be approximated by sampling the state s t−1 and the action x t taken by WORKER .
[ reference ] . The gradient with respect to the non - differentiable term can be estimated with REINFORCE williams1992simple , while the term can be trained using ordinary backpropagation . section : Experiments
However , at test time , these models do not produce a single token but a whole sequence . In order to resolve this inconsistency and to potentially improve generation , recent work has focused on training these models at the sequence - level , for instance using REINFORCE ranzato2015sequence , actor - critic bahdanau2016ac , or with beam search optimization wiseman2016acl . Before the recent work on sequence level training for neural networks , there has been a large body of research on training linear models at the sequence level .
Our proposed methodology borrows ideas from the reinforcement learning literature sutton - rl . In particular , we build on the REINFORCE algorithm proposed by reinforce , to achieve the above two objectives . While sampling from the model during training is quite a natural step for the REINFORCE algorithm , optimizing directly for any test metric can also be achieved by it .
Moreover , MIXER is several times faster since it relies only on greedy search . It is worth mentioning that the REINFORCE baseline did not work for these applications . Exploration from a random policy has little chance of success .
This gives rise to the challenging problem of determining the target for the next network output . bengio2015scheduled use the token from the ground - truth answer as the target for the network at step , whereas ranzato2015sequence rely on the REINFORCE algorithm williams1992simple to decide whether or not the tokens from a sampled prediction lead to a high task - specific score , such as BLEU papineni2002bleu or ROUGE lin2003automatic . In this work , we propose and study an alternative procedure for training sequence prediction networks that aims to directly improve their test time metrics ( which are typically not the log - likelihood ) .
The parameteric estimate is called the critic . The above formula is similar in spirit to the REINFORCE learning rule that ranzato2015sequence use in the same context : where the scalar is called baseline or control variate . The difference is that in REINFORCE the inner sum over all actions is replaced by its 1 - sample estimate , namely , where the log probability is introduced to correct for the sampling of .
The motivation behind this approach is that using the ground - truth output should produce a better baseline that lowers the variance of REINFORCE , resulting in higher task - specific scores . We refer to this method as REINFORCE - critic . subsection : Spelling Correction
For training the ASDN , since we apply the sampling strategy to convert the heatmap into a binary mask , which is not differentiable , we can not directly back - prop the gradients from the classification loss . Alternatively , we take the inspirations from the REINFORCE approach . We compute which binary masks lead to significant drops in Fast - RCNN classification scores .
This allows the policy network to be indirectly improved through Q - learning over the off - policy trajectories . Our method is in sharp contrast to existing RL - based methods for KBC , which use a policy gradients ( REINFORCE ) method and usually require a large number of rollouts to obtain a trajectory with a positive reward , especially in the early stages of learning . Experimental results on several benchmarks , including a synthetic task and several real - world KBC tasks , show that our approach learns better policies than previous RL - based methods and traditional KBC methods .
We ran the experiments three times and report the means and standard deviations ( except for PRA , TransE , and TransR on NELL995 , whose results are directly quoted from ) . On the Three Glass Puzzle task , M - Walk significantly outperforms the baseline : the best model of M - Walk achieves an accuracy of while the best REINFORCE method achieves ( see Appendix [ reference ] for more experiments with different settings on this task ) . For the two KBC tasks , we report their results in Tables [ reference ] - [ reference ] , where PG - Walk and Q - Walk are two methods we created just for the ablation study in the next section .
It shows that M - Walk converges much faster than PG and achieves better results on this task . In Table [ reference ] , we report the test accuracy of M - Walk and vanilla policy gradient ( REINFORCE / PG ) with different beam search sizes and different MCTS rollouts during testing . The number of MCTS simulations for training M - Walk is fixed to be .
Gradients also backpropagate through the gating network to its inputs . Our method differs here from [ reference ] who use boolean gates and a REINFORCE - style approach to train the gating network . section : ADDRESSING PERFORMANCE CHALLENGES
As the target Q function is changing slowly , we combine the one-step estimate with zero-step estimate to avoid sharp changes . The experimental results illustrate that CPG outperforms TD3 and some other reinforcement learning methods on multiple MuJoCo benchmarks .
In this current research , Twin-Delayed DDPG ( TD3 ) algorithm has been used to solve the most challenging virtual Artificial Intelligence application by training a 4-ant-legged robot as an Intelligent Agent to run across a field . Twin-Delayed DDPG ( TD3 ) is an incredibly smart AI model of a Deep Reinforcement Learning which combines the state-of-the-art methods in Artificial Intelligence . These includes Policy gradient , Actor-Critics , and continuous Double Deep Q-Learning .
In this current research work , we applied a Twin- Delayed DDPG ( TD3 ) algorithm to solve the most challenging virtual Artificial Intelligence application by training a HalfCheetah robot as an Intelligent Agent to run across a field . Twin-Delayed DDPG ( TD3 ) is a recent breakthrough smart AI model of a Deep Reinforcement Learning which combines the state-of-the-art techniques in Artificial Intelligence , including continuous Double Deep Q-Learning , Policy Gradient and Actor-Critic . These Deep Reinforcement Learning approaches have the capabilities to train an Intelligent agent to interact with an environment with automatic feature engineering , that is , requiring minimal domain knowledge . Twin-Delayed Deep Deterministic Policy Gradient algorithm ( TD3 ) was built on the Deep Deterministic Policy Gradient algorithm ( DDPG ) . During the implementation of the TD3 model , we used a two- layer feedforward neural network of 400 and 300 hidden nodes respectively , with Rectified Linear Units ( ReLU ) as an activation function between each layer for both the Actor and Critics , and then a final tanh unit following the output of the Actor .
The Critic network evaluates the confidence of the new bounding box online to determine whether the Critic model needs to be updated or re-initialized . In further , in our model we use TD3 algorithm to further optimize the AC model by using two Critic networks to jointly predict the bounding box confidence , and to obtain the smaller predicted value as the label to update the network parameters , thereby rendering the Critic network to avoid excessive estimation bias , accelerate the convergence of the loss function , and obtain more accurate prediction values . Also , a small amount of random noise with upper and lower bounds are added to the action in the Actor model , and the search area is reasonably expanded in offline learning to improve the robustness of the tracking method under strong background interference and fast object motion .
MVPI enjoys great flexibility in that any policy evaluation method and risk-neutral control method can be dropped in for risk-averse control off the shelf , in both on- and off-policy settings . We propose risk-averse TD3 as an example instantiating MVPI , which outperforms vanilla TD3 and many previous risk-averse control methods in challenging Mujoco robot simulation tasks under a risk-aware performance metric . This risk-averse TD3 is the first to introduce deterministic policies and off-policy learning into risk-averse reinforcement learning , both of which are key to the performance boost we show in Mujoco domains . MVPI adopts a per-step reward perspective ( Bisi et al. , 2019 ) for risk-averse control , instead of the commonly used total reward perspective .
In this study , image acquisition process through hardware-software are shown . Hardware acquisition of a 200 GB capacity hard disk is made through Tableau TD3 and CRU Ditto . The images of the same storage are taken through Tableau , CRU and RTX USB bridge and through FTK imager and Forensic Imager ; then comparative performance assessment results are presented .
This work proposes the use of reinforcement learning ( RL ) techniques with deep-learning models to address the maximum power point tracking ( MPPT ) control problem of a photovoltaic ( PV ) array . We implemented the deep deterministic policy gradient ( DDPG ) method , the inverted gradient ( IGDDPG ) method and the delayed twins ( TD3 ) method to solve the MPPT control problem . Several simulation experiments were performed in the OpenAI Gym platform aiming to evaluate the performance of the proposed control strategies , under different operating conditions in terms of temperature and solar irradiance .
Our mechanism builds on Double Q-learning , by mixing update action value based on the minimum and maximum between a pair of critics to limit the overestimation . We then propose a specific adaptation to the Twin Delayed Deep Deterministic policy gradient algorithm ( TD3 ) and show that the resulting algorithm not only reduces the observed overestimations , as hypothesized , but that this also leads to much better performance on several tasks .
We further investigate the robustness of these tests to violations of the most common hypotheses ( normal distributions , same distributions , equal variances ) . Beside simulations , we compare empirical distributions obtained by running Soft-Actor Critic and Twin-Delayed Deep Deterministic Policy Gradient on Half-Cheetah . We conclude by providing guidelines and code to perform rigorous comparisons of RL algorithm performances .
It is well known that Challenges such as background clutters , fast object motion and occlusion et al. affect a lot the robustness or accuracy of existing object tracking methods . This paper proposes a reinforcement learning model based on Twin Delayed Deep Deterministic algorithm ( TD3 ) for single object tracking . The model is based on the deep reinforcement learning model , Actor-Critic ( AC ) , in which the Actor network predicts a continuous action that moves the target bounding box in the previous frame to the object position in the current frame and adapts to the object size .
Gnu-RL: A Precocial Reinforcement Learning Solution for Building HVAC Control Using a Differentiable MPC Policy Epistemic Risk-Sensitive Reinforcement Learning Policy-Based Reinforcement Learning Approaches SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient Towards Generalization and Efficiency in Reinforcement Learning ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks Gradient-based Reinforcement Planning in Policy-Search Methods Self-Critical Sequence Training for Image Captioning Deep Reinforcement Learning for Mention-Ranking Coreference Models Resource Management for Efficient, Scalable and Resilient Network Function Chains Evaluation of state representation methods in robot hand-eye coordination learning from demonstration Timing and Parameter Optimization for One-time Motion Problem Based on Reinforcement Learning Recurrent Attentional Model for No-Reference Image Quality Assessment Adversarial Policy Gradient for Alternating Markov Games Stein Variational Policy Gradient Long Text Generation via Adversarial Training with Leaked Information Non-Autoregressive Neural Machine Translation Classical Structured Prediction Losses for Sequence to Sequence Learning Sequence Level Training with Recurrent Neural Networks An Actor-Critic Algorithm for Sequence Prediction A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer Conservative Policy Gradient in Multi-critic Setting Twin-Delayed DDPG: A Deep Reinforcement Learning Technique to Model a Continuous Movement of an Intelligent Robot Agent Modeling a Continuous Locomotion Behavior of an Intelligent Agent Using Deep Reinforcement Technique. Real-Time Visual Object Tracking Based on Reinforcement Learning with Twin Delayed Deep Deterministic Algorithm Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning Forensics Image Acquisition Process of Digital Evidence MPPT for PV systems using deep reinforcement learning algorithms Mixing Update Q-value for Deep Reinforcement Learning A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms Real-Time Visual Object Tracking Based on Reinforcement Learning with Twin Delayed Deep Deterministic Algorithm
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BuildSys@SenSys AAAI ICLR (Poster) ICLR (Workshop) AAAI ICLR NAACL-HLT ICLR ICLR 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NeurIPS ICLR ICVISP IScIDE (1) IJCNN IScIDE (1)
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