Papers Policy Gradient Methods
“Policy Gradient Methods” 태그가 달린 논문 382편 · 필터 해제
Optimal Resource Allocation in Wireless Control Systems via Deep Policy Gradient
In wireless control systems, remote control of plants is achieved through closing of the control loop over a wireless channel. As wireless communication is noisy and subject to packet dropouts, proper allocation of limit…
Deep Reinforcement LearningPolicy Gradient MethodsPolicy Optimization for $\mathcal{H}_2$ Linear Control with $\mathcal{H}_\infty$ Robustness Guarantee: Implicit Regularization and Global Convergence
Policy optimization (PO) is a key ingredient for reinforcement learning (RL). For control design, certain constraints are usually enforced on the policies to optimize, accounting for either the stability, robustness, or …
Policy Gradient MethodsReinforcement LearningReinforcement Learning (RL)All-Action Policy Gradient Methods: A Numerical Integration Approach
While often stated as an instance of the likelihood ratio trick [Rubinstein, 1989], the original policy gradient theorem [Sutton, 1999] involves an integral over the action space. When this integral can be computed, the …
Allcontinuous-controlContinuous ControlNumerical Integration+1Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods
We investigate reinforcement learning in the setting of Markov decision processes for a large number of exchangeable agents interacting in a mean field manner. Applications include, for example, the control of a large nu…
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control
Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting. However, policy gradients can …
continuous-controlContinuous ControlDeep Reinforcement LearningOpenAI Gym+2Policy Tree Network
Decision-time planning policies with implicit dynamics models have been shown to work in discrete action spaces with Q learning. However, decision-time planning with implicit dynamics models in continuous action space ha…
Model-based Reinforcement LearningMuJoCoPolicy Gradient MethodsQ-Learning+3Guided Adaptive Credit Assignment for Sample Efficient Policy Optimization
Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems. However, it still often suffers from sparse reward tasks, which leads to poor sample efficiency during tr…
Instruction FollowingPolicy Gradient MethodsAUGMENTED POLICY GRADIENT METHODS FOR EFFICIENT REINFORCEMENT LEARNING
We propose a new mixture of model-based and model-free reinforcement learning (RL) algorithms that combines the strengths of both RL methods. Our goal is to reduce the sample complexity of model-free approaches utilizing…
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Sample Efficient Policy Gradient Methods with Recursive Variance Reduction
Improving the sample efficiency in reinforcement learning has been a long-standing research problem. In this work, we aim to reduce the sample complexity of existing policy gradient methods. We propose a novel policy gra…
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learning
This paper addresses the problem of legged locomotion in non-flat terrain. As legged robots such as quadrupeds are to be deployed in terrains with geometries which are difficult to model and predict, the need arises to e…
Deep Reinforcement LearningMotion PlanningPolicy Gradient Methodsreinforcement-learning+2Deep Reinforcement Learning Algorithm for Dynamic Pricing of Express Lanes with Multiple Access Locations
This article develops a deep reinforcement learning (Deep-RL) framework for dynamic pricing on managed lanes with multiple access locations and heterogeneity in travelers' value of time, origin, and destination. This fra…
Deep Reinforcement LearningPolicy Gradient MethodsReinforcement LearningReinforcement Learning (RL)Transfer Reward Learning for Policy Gradient-Based Text Generation
Task-specific scores are often used to optimize for and evaluate the performance of conditional text generation systems. However, such scores are non-differentiable and cannot be used in the standard supervised learning …
Conditional Text GenerationImage CaptioningPolicy Gradient MethodsSemantic Similarity+4Multi Pseudo Q-learning Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles
This paper investigates trajectory tracking problem for a class of underactuated autonomous underwater vehicles (AUVs) with unknown dynamics and constrained inputs. Different from existing policy gradient methods which e…
Policy Gradient MethodsQ-LearningNeural Policy Gradient Methods: Global Optimality and Rates of Convergence
Policy gradient methods with actor-critic schemes demonstrate tremendous empirical successes, especially when the actors and critics are parameterized by neural networks. However, it remains less clear whether such "neur…
Policy Gradient MethodsTrajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods
Policy gradient methods have demonstrated success in reinforcement learning tasks that have high-dimensional continuous state and action spaces. However, policy gradient methods are also notoriously sample inefficient. T…
Policy Gradient MethodsReinforcement LearningHealth-Informed Policy Gradients for Multi-Agent Reinforcement Learning
This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the…
Multi-agent Reinforcement LearningPolicy Gradient Methodsreinforcement-learningReinforcement Learning+1On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift
Policy gradient methods are among the most effective methods in challenging reinforcement learning problems with large state and/or action spaces. However, little is known about even their most basic theoretical converge…
Policy Gradient MethodsReinforcement LearningHindsight Trust Region Policy Optimization
Reinforcement Learning(RL) with sparse rewards is a major challenge. We propose \emph{Hindsight Trust Region Policy Optimization}(HTRPO), a new RL algorithm that extends the highly successful TRPO algorithm with \emph{hi…
Atari GamesPolicy Gradient MethodsReinforcement LearningReinforcement Learning (RL)Variance Reduction in Actor Critic Methods (ACM)
After presenting Actor Critic Methods (ACM), we show ACM are control variate estimators. Using the projection theorem, we prove that the Q and Advantage Actor Critic (A2C) methods are optimal in the sense of the $L^2$ no…
Policy Gradient MethodsShapley Q-value: A Local Reward Approach to Solve Global Reward Games
Cooperative game is a critical research area in the multi-agent reinforcement learning (MARL). Global reward game is a subclass of cooperative games, where all agents aim to maximize the global reward. Credit assignment …
Multi-agent Reinforcement LearningPolicy Gradient MethodsReinforcement Learning