The first step is token-level training using the Maximum-Likelihood Estimation ( MLE ) as the objective function . Next , at completion of the token-level training , we advance to a sequence-level training based on a new objective function inspired by the policy gradient algorithm from reinforcement learning . Our design also overcomes the exposure bias problem by closing the feedback loop in the decoder during sequence-level training , i.e. , feeding in the predicted token instead of the ground truth token at every time step . In this work , the use of SNNs as stochastic policies is explored under an energy-efficient first-to-spike action rule , whereby the action taken by the RL agent is determined by the occurrence of the first spike among the output neurons . A policy gradient-based algorithm is derived considering a Generalized Linear Model ( GLM ) for spiking neurons . Experimental results demonstrate the capability of online trained SNNs as stochastic policies to gracefully trade energy consumption , as measured by the number of spikes , and control performance . Our method optimistically explores actions to leverage potential repeated structure in the procedure . In comparing to some state-of-the-art approaches we find that our explicit procedural learning from observation method is about 100 times faster than policy-gradient based approaches that learn a stochastic policy and is faster than model based approaches as well . We also find that performing optimistic action selection yields substantial speed ups when latent dynamical structure is present . To solve the problem that the gradient can not pass back to the generative model when the output is discrete , we regard the generative model as a stochastic parametrized policy . In our policy gradient , we employ Monte Carlo ( MC ) search to approximate the state - action value . We directly train the policy ( generative model ) via policy gradient , which naturally avoids the differentiation difficulty for discrete data in a conventional GAN . This sampling allows SPG to search the action-Q-value space more globally than DPG , enabling it to theoretically avoid more local optima . Q-Learning is compared against the actor-critic algorithms CACLA , DPG , and the novel SPG in a pellet collection and a self play environment . Results show that Q-Learning and CACLA outperform a pre-programmed greedy bot in the pellet collection task , but all algorithms fail to outperform this bot in a fighting scenario . The approach provides stability automatically ; there is no need for carefully derived numerical fluxes ( as in DG schemes ) or for mesh-dependent stabilization terms ( as in stabilized methods ) . In this paper , we focus on features of Camellia that facilitate implementation of new DPG formulations ; chief among these is a rich set of features in support of symbolic manipulation , which allow , e.g. , bilinear formulations in the code to appear much as they would on paper . Many of these features are general in the sense that they can also be used in the implementation of other finite element formulations . This study presents a survey of control and management techniques for micro-grid consisting of various DPG units . Herein , we summarizes several management schemes and methodologies used for the DPG inverter that decomposes the control downside into steady-state and transient sub issues so as to cut back the general computation time in order to form the stability of power distribution system with additive responsiveness . Keywords : Energy management , microgrid , renewable power source Cite this Article Kirti Yadav , Uma Shankar Patel . In the context of learning deterministic policies in continuous domains , we revisit an approach , which was first proposed in Continuous Actor Critic Learning Automaton ( CACLA ) and later extended in Neural Fitted Actor Critic ( NFAC ) . This approach is based on a policy update different from that of deterministic policy gradient ( DPG ) . Previous work has observed its excellent performance empirically , but a theoretical justification is lacking . Both controllers are designed in continuous state and action spaces . In our study , two complementary techniques are proposed within the framework of deterministic policy gradient ( DPG ) . First , the advantage function is introduced in the critic network to improve the performance of the learning process . We thank the Editor-in-Chief of CAMWA , Leszek F. Demkowicz , ICES , UT Austin , Texas , USA , for his encouragement to prepare this special issue , and the Hausdorff Center for Mathematics , University of Bonn for the generous financial support , which made the workshop possible . The special issue contains a representative collection of articles dealing with recent advances in the field of high-order Finite Element and spectral approximation for PDEs and related topics ranging from the regularity theory , fundamental properties of high-order approximation operators , the numerical hp-approximation of boundary layers to the recent progress in different types of hp-adaptive methods ( including Legendre – Galerkin , conforming hp-FEM and hp-DGFEM ) , Discontinuous Petrov – Galerkin ( DPG ) methods , Stochastic Galerkin methods for forward uncertainty propagation , wavenumber explicit error analyses for the Helmholtz problem , and size-robust numerical methods for photonic crystals . We now provide a brief survey of the content of this special issue containing fifteen articles . Testing of the digital pulse generator ( DPG ) , which is part o f the RDU , showed a drop in the specified output power of 10 dB. The power budget calculations made for the transmitte r were made based on the input power of the DPG . The DPG unit also failed to provide adequate port to port isolati n between the input trigger and the I & Q output channels . Spurious harmonics signals from the trigger resulted in ali asing with the IF frequencies . We also show that standard DPG , while not guaranteed to be conservative , is nearly conservative for many of the benchmarks considered . The new method preserves many of the attractive features of DPG , but turns the normally symmetric positive-definite DPG system into a saddle point problem . Original Immulite sample cup holder with DPC sample cup ( A ) and Eemland-style sample-cup holder with common secondary sample tube ( B ) . ( 1 ) Sample-cup holder ; ( 2 ) DPG sample cup ; (3)secondary sample tube . is a versatile continuous random-access chemilumi-nescence immunoassay system . The proposed dual-polarity GRAPPA ( DPG ) method estimates missing k-space data while simultaneously correcting inherent EPI phase errors . RESULTS Simulation results showed that standard NGC is incapable of correcting higher-order phase errors , whereas the DPG kernel approach successfully removed these errors . The presence of higher-order phase errors near regions of local susceptibility gradients was demonstrated with in vivo data . Low policies allow exploring deeper in the environment , while high policies prevent over - specialization . subsection : Ape - X DPG To test the generality of the framework we also combined it with a continuous - action policy gradient system based on DDPG ddpg , an implementation of deterministic policy gradients also similar to older methods adhdp , acd , and tested it on continuous control tasks from the DeepMind Control Suite tassa2018suite . Subsequently , to calculate the loss function with more accurate target value for the chosen critic , Pseudo Q-learning , which uses subgreedy policy to replace the greedy policy in Q-learning , is developed for continuous action spaces , and Multi Pseudo Q-learning ( MPQ ) is proposed to reduce the overestimation of action-value function and to stabilize the learning . As for the actors , deterministic policy gradient is applied to update the weights , and the final learned policy is defined as the average of all actors to avoid large but bad updates . Moreover , the stability analysis of the learning is given qualitatively . This paper studies the problem of formation control of multi-agent systems with the reinforcement learning method . A novel multi-agent formation control algorithm is first proposed , which adopts the framework of centralized training with decentralized execution , and combines the deterministic policy gradient ( DPG ) method with multi-agent advantage function . Then , three scenarios under partial observable Markov games are presented to study the multi-agent formation control problem and verify the proposed algorithm . In the context of learning deterministic policies in continuous domains , we revisit an approach , which was first proposed in Continuous Actor Critic Learning Automaton ( CACLA ) and later extended in Neural Fitted Actor Critic ( NFAC ) . This approach is based on a policy update different from that of deterministic policy gradient ( DPG ) . Previous work has observed its excellent performance empirically , but a theoretical justification is lacking . The key goal is to self-learn the optimal strategy in the continuous action domain and obtain a specific physical meaning of the fuzzy rules . The novel proposed algorithm is based on the deterministic policy gradient theorem and the agent learns the near-optimal strategy under the actor-critic structure . The fuzzy inference system is applied as approximators so that the specific physical meaning can be obtained by the linguistic fuzzy rules . However , allocating bandwidth in a finer resolution usually implies larger action space , and unfortunately DQL fails to quickly converge in this case . In this letter , we introduce discrete normalized advantage functions ( DNAF ) into DQL , by separating the < inline-formula > < tex-math notation="LaTeX">$\mathcal {Q}$ < /tex-math></inline-formula>-value function as a state-value function term and an advantage term and exploiting a deterministic policy gradient descent ( DPGD ) algorithm to avoid the unnecessary calculation of < inline-formula > < tex-math notation="LaTeX">$\mathcal {Q}$ < /tex-math></inline-formula>-value for every state-action pair . Furthermore , as DPGD only works in continuous action space , we embed a k-nearest neighbor algorithm into DQL to quickly find a valid action in the discrete space nearest to the DPGD output . When the mathematical model of agent is unknown , it 's effective to use machine learning algorithm to design the policy of each agent . According to the cooperation among pursuers and competition between evaders and pursuers , we choose deterministic policy gradient of reinforcement learning as our basic approach . In this study , we redesign the reward function and the structure of neural network to adapt to the actual environment where evader has greater speed and accelerated speed than pursuers . We learn to imitate an expert parsing oracle , such that our neural parser can generalize to parse natural images without ground truth . We introduce a novel deterministic policy gradient update , DRAG ( i.e. , DeteRministically AGgrevate ) in the form of a deterministic actor-critic variant of AggreVaTeD , to train our neural parser . From another perspective , our approach is a variant of the Deterministic Policy Gradient suitable for the imitation learning setting . However such compound policy may get biased towards a task or the gradients from different tasks negate each other , making the learning unstable and sometimes less data efficient . In this paper , we propose a new approach for simultaneous training of multiple tasks sharing a set of common actions in continuous action spaces , which we call as DiGrad ( Differential Policy Gradient ) . The proposed framework is based on differential policy gradients and can accommodate multi-task learning in a single actor-critic network . [ reference ][ reference ] . It uses an efficient deterministic gradient - based update to iteratively evolve a set of particles { x i } n i=1 to minimize the KL divergence with the target distribution . SVGD has a simple form that reduces to the typical gradient descent for maximizing log p when using only one particle ( n = 1 ) , and hence can be easily combined with the successful tricks for gradient optimization , including stochastic gradient , adaptive learning rates ( such as adagrad ) , and momentum . this work π : O → A is a deterministic policy ; given an observation it will always output the same action . However , π could be extended to stochastic policies . subsection : Ape - X DPG To test the generality of the framework we also combined it with a continuous - action policy gradient system based on DDPG ddpg , an implementation of deterministic policy gradients also similar to older methods adhdp , acd , and tested it on continuous control tasks from the DeepMind Control Suite tassa2018suite . The Ape - X DPG setup is similar to Ape - X DQN , but the actor ’s policy is now represented explicitly by a separate policy network , in addition to the Q - network . The Mean Field Regime . We study an interacting particle system in $ \mathbf{R}^d$ motivated by Stein variational gradient descent [ Q. Liu and D. Wang , NIPS 2016 ] , a deterministic algorithm for sampling from a given probability density with unknown normalization . We prove that in the large particle limit the empirical measure of the particle system converges to a solution of a non-local and nonlinear PDE . Scalable Bayesian sampling is playing an important role in modern machine learning , especially in the fast-developed unsupervised-(deep)-learning models . While tremendous progresses have been achieved via scalable Bayesian sampling such as stochastic gradient MCMC ( SG-MCMC ) and Stein variational gradient descent ( SVGD ) , the generated samples are typically highly correlated . Moreover , their sample-generation processes are often criticized to be inefficient . In this work , we introduce a maximum entropy policy optimization framework which explicitly encourages parameter exploration , and show that this framework can be reduced to a Bayesian inference problem . We then propose a novel Stein variational policy gradient method ( SVPG ) which combines existing policy gradient methods and a repulsive functional to generate a set of diverse but well-behaved policies . SVPG is robust to initialization and can easily be implemented in a parallel manner . Experimental results indicate that our algorithm works comparable to existing algorithms in environments with dense rewards , and significantly better in environments with sparse and episodic rewards . We then discuss limitations of self-imitation learning , and propose to solve them by using Stein variational policy gradient descent with the Jensen-Shannon kernel to learn multiple diverse policies . We demonstrate its effectiveness on a challenging variant of continuous-control MuJoCo locomotion tasks . Other more traditional approaches for training energy - based models ( e.g. , [ reference ][ reference ] are often based on variants of MCMC - MLE or contrastive divergence [ reference ][ reference ][ reference ] , and have difficulty generating realistic - looking images from scratch . section : STEIN VARIATIONAL GRADIENT DESCENT ( SVGD ) Stein variational gradient descent ( SVGD ) [ reference ] ) is a general purpose Bayesian inference algorithm motivated by Stein 's method [ reference ][ reference ] and kernelized Stein discrepancy [ reference ][ reference ] . = f ( η t ; ξ i ) , and the Stein variational gradient ∆x i in ( 7 ) . We then propose a novel Stein variational policy gradient method ( SVPG ) which combines existing policy gradient methods and a repulsive functional to generate a set of diverse but well-behaved policies . 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 .
The first step is token-level training using the Maximum-Likelihood Estimation ( MLE ) as the objective function . Next , at completion of the token-level training , we advance to a sequence-level training based on a new objective function inspired by the policy gradient algorithm from reinforcement learning . Our design also overcomes the exposure bias problem by closing the feedback loop in the decoder during sequence-level training , i.e. , feeding in the predicted token instead of the ground truth token at every time step .
In this work , the use of SNNs as stochastic policies is explored under an energy-efficient first-to-spike action rule , whereby the action taken by the RL agent is determined by the occurrence of the first spike among the output neurons . A policy gradient-based algorithm is derived considering a Generalized Linear Model ( GLM ) for spiking neurons . Experimental results demonstrate the capability of online trained SNNs as stochastic policies to gracefully trade energy consumption , as measured by the number of spikes , and control performance .
Our method optimistically explores actions to leverage potential repeated structure in the procedure . In comparing to some state-of-the-art approaches we find that our explicit procedural learning from observation method is about 100 times faster than policy-gradient based approaches that learn a stochastic policy and is faster than model based approaches as well . We also find that performing optimistic action selection yields substantial speed ups when latent dynamical structure is present .
To solve the problem that the gradient can not pass back to the generative model when the output is discrete , we regard the generative model as a stochastic parametrized policy . In our policy gradient , we employ Monte Carlo ( MC ) search to approximate the state - action value . We directly train the policy ( generative model ) via policy gradient , which naturally avoids the differentiation difficulty for discrete data in a conventional GAN .
This sampling allows SPG to search the action-Q-value space more globally than DPG , enabling it to theoretically avoid more local optima . Q-Learning is compared against the actor-critic algorithms CACLA , DPG , and the novel SPG in a pellet collection and a self play environment . Results show that Q-Learning and CACLA outperform a pre-programmed greedy bot in the pellet collection task , but all algorithms fail to outperform this bot in a fighting scenario .
The approach provides stability automatically ; there is no need for carefully derived numerical fluxes ( as in DG schemes ) or for mesh-dependent stabilization terms ( as in stabilized methods ) . In this paper , we focus on features of Camellia that facilitate implementation of new DPG formulations ; chief among these is a rich set of features in support of symbolic manipulation , which allow , e.g. , bilinear formulations in the code to appear much as they would on paper . Many of these features are general in the sense that they can also be used in the implementation of other finite element formulations .
This study presents a survey of control and management techniques for micro-grid consisting of various DPG units . Herein , we summarizes several management schemes and methodologies used for the DPG inverter that decomposes the control downside into steady-state and transient sub issues so as to cut back the general computation time in order to form the stability of power distribution system with additive responsiveness . Keywords : Energy management , microgrid , renewable power source Cite this Article Kirti Yadav , Uma Shankar Patel .
In the context of learning deterministic policies in continuous domains , we revisit an approach , which was first proposed in Continuous Actor Critic Learning Automaton ( CACLA ) and later extended in Neural Fitted Actor Critic ( NFAC ) . This approach is based on a policy update different from that of deterministic policy gradient ( DPG ) . Previous work has observed its excellent performance empirically , but a theoretical justification is lacking .
Both controllers are designed in continuous state and action spaces . In our study , two complementary techniques are proposed within the framework of deterministic policy gradient ( DPG ) . First , the advantage function is introduced in the critic network to improve the performance of the learning process .
We thank the Editor-in-Chief of CAMWA , Leszek F. Demkowicz , ICES , UT Austin , Texas , USA , for his encouragement to prepare this special issue , and the Hausdorff Center for Mathematics , University of Bonn for the generous financial support , which made the workshop possible . The special issue contains a representative collection of articles dealing with recent advances in the field of high-order Finite Element and spectral approximation for PDEs and related topics ranging from the regularity theory , fundamental properties of high-order approximation operators , the numerical hp-approximation of boundary layers to the recent progress in different types of hp-adaptive methods ( including Legendre – Galerkin , conforming hp-FEM and hp-DGFEM ) , Discontinuous Petrov – Galerkin ( DPG ) methods , Stochastic Galerkin methods for forward uncertainty propagation , wavenumber explicit error analyses for the Helmholtz problem , and size-robust numerical methods for photonic crystals . We now provide a brief survey of the content of this special issue containing fifteen articles .
Testing of the digital pulse generator ( DPG ) , which is part o f the RDU , showed a drop in the specified output power of 10 dB. The power budget calculations made for the transmitte r were made based on the input power of the DPG . The DPG unit also failed to provide adequate port to port isolati n between the input trigger and the I & Q output channels . Spurious harmonics signals from the trigger resulted in ali asing with the IF frequencies .
We also show that standard DPG , while not guaranteed to be conservative , is nearly conservative for many of the benchmarks considered . The new method preserves many of the attractive features of DPG , but turns the normally symmetric positive-definite DPG system into a saddle point problem .
Original Immulite sample cup holder with DPC sample cup ( A ) and Eemland-style sample-cup holder with common secondary sample tube ( B ) . ( 1 ) Sample-cup holder ; ( 2 ) DPG sample cup ; (3)secondary sample tube . is a versatile continuous random-access chemilumi-nescence immunoassay system .
The proposed dual-polarity GRAPPA ( DPG ) method estimates missing k-space data while simultaneously correcting inherent EPI phase errors . RESULTS Simulation results showed that standard NGC is incapable of correcting higher-order phase errors , whereas the DPG kernel approach successfully removed these errors . The presence of higher-order phase errors near regions of local susceptibility gradients was demonstrated with in vivo data .
Low policies allow exploring deeper in the environment , while high policies prevent over - specialization . subsection : Ape - X DPG To test the generality of the framework we also combined it with a continuous - action policy gradient system based on DDPG ddpg , an implementation of deterministic policy gradients also similar to older methods adhdp , acd , and tested it on continuous control tasks from the DeepMind Control Suite tassa2018suite .
Subsequently , to calculate the loss function with more accurate target value for the chosen critic , Pseudo Q-learning , which uses subgreedy policy to replace the greedy policy in Q-learning , is developed for continuous action spaces , and Multi Pseudo Q-learning ( MPQ ) is proposed to reduce the overestimation of action-value function and to stabilize the learning . As for the actors , deterministic policy gradient is applied to update the weights , and the final learned policy is defined as the average of all actors to avoid large but bad updates . Moreover , the stability analysis of the learning is given qualitatively .
This paper studies the problem of formation control of multi-agent systems with the reinforcement learning method . A novel multi-agent formation control algorithm is first proposed , which adopts the framework of centralized training with decentralized execution , and combines the deterministic policy gradient ( DPG ) method with multi-agent advantage function . Then , three scenarios under partial observable Markov games are presented to study the multi-agent formation control problem and verify the proposed algorithm .
In the context of learning deterministic policies in continuous domains , we revisit an approach , which was first proposed in Continuous Actor Critic Learning Automaton ( CACLA ) and later extended in Neural Fitted Actor Critic ( NFAC ) . This approach is based on a policy update different from that of deterministic policy gradient ( DPG ) . Previous work has observed its excellent performance empirically , but a theoretical justification is lacking .
The key goal is to self-learn the optimal strategy in the continuous action domain and obtain a specific physical meaning of the fuzzy rules . The novel proposed algorithm is based on the deterministic policy gradient theorem and the agent learns the near-optimal strategy under the actor-critic structure . The fuzzy inference system is applied as approximators so that the specific physical meaning can be obtained by the linguistic fuzzy rules .
However , allocating bandwidth in a finer resolution usually implies larger action space , and unfortunately DQL fails to quickly converge in this case . In this letter , we introduce discrete normalized advantage functions ( DNAF ) into DQL , by separating the < inline-formula > < tex-math notation="LaTeX">$\mathcal {Q}$ < /tex-math></inline-formula>-value function as a state-value function term and an advantage term and exploiting a deterministic policy gradient descent ( DPGD ) algorithm to avoid the unnecessary calculation of < inline-formula > < tex-math notation="LaTeX">$\mathcal {Q}$ < /tex-math></inline-formula>-value for every state-action pair . Furthermore , as DPGD only works in continuous action space , we embed a k-nearest neighbor algorithm into DQL to quickly find a valid action in the discrete space nearest to the DPGD output .
When the mathematical model of agent is unknown , it 's effective to use machine learning algorithm to design the policy of each agent . According to the cooperation among pursuers and competition between evaders and pursuers , we choose deterministic policy gradient of reinforcement learning as our basic approach . In this study , we redesign the reward function and the structure of neural network to adapt to the actual environment where evader has greater speed and accelerated speed than pursuers .
We learn to imitate an expert parsing oracle , such that our neural parser can generalize to parse natural images without ground truth . We introduce a novel deterministic policy gradient update , DRAG ( i.e. , DeteRministically AGgrevate ) in the form of a deterministic actor-critic variant of AggreVaTeD , to train our neural parser . From another perspective , our approach is a variant of the Deterministic Policy Gradient suitable for the imitation learning setting .
However such compound policy may get biased towards a task or the gradients from different tasks negate each other , making the learning unstable and sometimes less data efficient . In this paper , we propose a new approach for simultaneous training of multiple tasks sharing a set of common actions in continuous action spaces , which we call as DiGrad ( Differential Policy Gradient ) . The proposed framework is based on differential policy gradients and can accommodate multi-task learning in a single actor-critic network .
[ reference ][ reference ] . It uses an efficient deterministic gradient - based update to iteratively evolve a set of particles { x i } n i=1 to minimize the KL divergence with the target distribution . SVGD has a simple form that reduces to the typical gradient descent for maximizing log p when using only one particle ( n = 1 ) , and hence can be easily combined with the successful tricks for gradient optimization , including stochastic gradient , adaptive learning rates ( such as adagrad ) , and momentum .
this work π : O → A is a deterministic policy ; given an observation it will always output the same action . However , π could be extended to stochastic policies .
subsection : Ape - X DPG To test the generality of the framework we also combined it with a continuous - action policy gradient system based on DDPG ddpg , an implementation of deterministic policy gradients also similar to older methods adhdp , acd , and tested it on continuous control tasks from the DeepMind Control Suite tassa2018suite . The Ape - X DPG setup is similar to Ape - X DQN , but the actor ’s policy is now represented explicitly by a separate policy network , in addition to the Q - network .
The Mean Field Regime . We study an interacting particle system in $ \mathbf{R}^d$ motivated by Stein variational gradient descent [ Q. Liu and D. Wang , NIPS 2016 ] , a deterministic algorithm for sampling from a given probability density with unknown normalization . We prove that in the large particle limit the empirical measure of the particle system converges to a solution of a non-local and nonlinear PDE .
Scalable Bayesian sampling is playing an important role in modern machine learning , especially in the fast-developed unsupervised-(deep)-learning models . While tremendous progresses have been achieved via scalable Bayesian sampling such as stochastic gradient MCMC ( SG-MCMC ) and Stein variational gradient descent ( SVGD ) , the generated samples are typically highly correlated . Moreover , their sample-generation processes are often criticized to be inefficient .
In this work , we introduce a maximum entropy policy optimization framework which explicitly encourages parameter exploration , and show that this framework can be reduced to a Bayesian inference problem . We then propose a novel Stein variational policy gradient method ( SVPG ) which combines existing policy gradient methods and a repulsive functional to generate a set of diverse but well-behaved policies . SVPG is robust to initialization and can easily be implemented in a parallel manner .
Experimental results indicate that our algorithm works comparable to existing algorithms in environments with dense rewards , and significantly better in environments with sparse and episodic rewards . We then discuss limitations of self-imitation learning , and propose to solve them by using Stein variational policy gradient descent with the Jensen-Shannon kernel to learn multiple diverse policies . We demonstrate its effectiveness on a challenging variant of continuous-control MuJoCo locomotion tasks .
Other more traditional approaches for training energy - based models ( e.g. , [ reference ][ reference ] are often based on variants of MCMC - MLE or contrastive divergence [ reference ][ reference ][ reference ] , and have difficulty generating realistic - looking images from scratch . section : STEIN VARIATIONAL GRADIENT DESCENT ( SVGD ) Stein variational gradient descent ( SVGD ) [ reference ] ) is a general purpose Bayesian inference algorithm motivated by Stein 's method [ reference ][ reference ] and kernelized Stein discrepancy [ reference ][ reference ] .
= f ( η t ; ξ i ) , and the Stein variational gradient ∆x i in ( 7 ) .
We then propose a novel Stein variational policy gradient method ( SVPG ) which combines existing policy gradient methods and a repulsive functional to generate a set of diverse but well-behaved policies . 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 .
Translating Mathematical Formula Images to LaTeX Sequences Using Deep Neural Networks with Sequence-level Training Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients PLOTS: Procedure Learning from Observations using Subtask Structure SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient Sampled Policy Gradient Compared to DPG, CACLA, and Q-Learning in the Game Agar.io Camellia: A Rapid Development Framework for Finite Element Solvers A Survey on Microgrid Power Control and Management Technique for Distributed Power Generator Units Exploiting the Sign of the Advantage Function to Learn Deterministic Policies in Continuous Domains Deterministic Policy Gradient with Advantage Function for Fixed Wing UAV Automatic Landing High-order finite element approximation for partial differential equations Integration, implementation and testing of the X-Band SASAR II system Locally conservative discontinuous Petrov-Galerkin finite elements for fluid problems Modified sample-cup holder improves work flow in immulite immunoassay system. Dual-polarity GRAPPA for simultaneous reconstruction and ghost correction of echo planar imaging data. Distributed Prioritized Experience Replay Multi Pseudo Q-Learning-Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles Deterministic Policy Gradient Based Formation Control for Multi-Agent Systems Exploiting the Sign of the Advantage Function to Learn Deterministic Policies in Continuous Domains A fuzzy deterministic policy gradient algorithm for pursuit-evasion differential games Deep Reinforcement Learning With Discrete Normalized Advantage Functions for Resource Management in Network Slicing Multi-Agent Cooperative-Competitive Environment with Reinforcement Learning Learning Neural Parsers with Deterministic Differentiable Imitation Learning DiGrad: Multi-Task Reinforcement Learning with Shared Actions Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning Deep Networks with Internal Selective Attention through Feedback Connections Distributed Prioritized Experience Replay Scaling Limit of the Stein Variational Gradient Descent: The Mean Field Regime Self-Adversarially Learned Bayesian Sampling Stein Variational Policy Gradient Learning Self-Imitating Diverse Policies Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning Stein Variational Policy Gradient
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