Papers Policy Gradient Methods
“Policy Gradient Methods” 태그가 달린 논문 382편 · 필터 해제
Control randomisation approach for policy gradient and application to reinforcement learning in optimal switching
We propose a comprehensive framework for policy gradient methods tailored to continuous time reinforcement learning. This is based on the connection between stochastic control problems and randomised problems, enabling a…
Policy Gradient MethodsActor-Critic Reinforcement Learning with Phased Actor
Policy gradient methods in actor-critic reinforcement learning (RL) have become perhaps the most promising approaches to solving continuous optimal control problems. However, the trial-and-error nature of RL and the inhe…
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)Intervention-Assisted Policy Gradient Methods for Online Stochastic Queuing Network Optimization: Technical Report
Deep Reinforcement Learning (DRL) offers a powerful approach to training neural network control policies for stochastic queuing networks (SQN). However, traditional DRL methods rely on offline simulations or static datas…
Deep Reinforcement LearningPolicy Gradient Methodsreinforcement-learningReinforcement LearningElementary Analysis of Policy Gradient Methods
Projected policy gradient under the simplex parameterization, policy gradient and natural policy gradient under the softmax parameterization, are fundamental algorithms in reinforcement learning. There have been a flurry…
Policy Gradient MethodsSelf-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement
Current methods for end-to-end constructive neural combinatorial optimization usually train a policy using behavior cloning from expert solutions or policy gradient methods from reinforcement learning. While behavior clo…
Combinatorial OptimizationImitation LearningJob Shop SchedulingPolicy Gradient Methods+2ReAct Meets ActRe: When Language Agents Enjoy Training Data Autonomy
Language agents have demonstrated autonomous decision-making abilities by reasoning with foundation models. Recently, efforts have been made to train language agents for performance improvement, with multi-step reasoning…
Policy Gradient MethodsTowards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
In the context of average-reward reinforcement learning, the requirement for oracle knowledge of the mixing time, a measure of the duration a Markov chain under a fixed policy needs to achieve its stationary distribution…
Policy Gradient MethodsGlobal Convergence Guarantees for Federated Policy Gradient Methods with Adversaries
Federated Reinforcement Learning (FRL) allows multiple agents to collaboratively build a decision making policy without sharing raw trajectories. However, if a small fraction of these agents are adversarial, it can lead …
Decision MakingPolicy Gradient MethodsTowards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis
Reinforcement Learning (RL) has shown exceptional performance across various applications, enabling autonomous agents to learn optimal policies through interaction with their environments. However, traditional RL framewo…
Policy Gradient MethodsReinforcement Learning (RL)Robot NavigationProvable Policy Gradient Methods for Average-Reward Markov Potential Games
We study Markov potential games under the infinite horizon average reward criterion. Most previous studies have been for discounted rewards. We prove that both algorithms based on independent policy gradient and independ…
Policy Gradient MethodsFill-and-Spill: Deep Reinforcement Learning Policy Gradient Methods for Reservoir Operation Decision and Control
Changes in demand, various hydrological inputs, and environmental stressors are among the issues that water managers and policymakers face on a regular basis. These concerns have sparked interest in applying different te…
Deep Reinforcement LearningPolicy Gradient MethodsStochastic OptimizationStabilizing Policy Gradients for Stochastic Differential Equations via Consistency with Perturbation Process
Considering generating samples with high rewards, we focus on optimizing deep neural networks parameterized stochastic differential equations (SDEs), the advanced generative models with high expressiveness, with policy g…
Drug DesignPolicy Gradient MethodsTowards Provable Log Density Policy Gradient
Policy gradient methods are a vital ingredient behind the success of modern reinforcement learning. Modern policy gradient methods, although successful, introduce a residual error in gradient estimation. In this work, we…
Policy Gradient Methodsreinforcement-learningReinforcement LearningReusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate
Reinforcement learning provides a mathematical framework for learning-based control, whose success largely depends on the amount of data it can utilize. The efficient utilization of historical trajectories obtained from …
Policy Gradient MethodsWhen Do Off-Policy and On-Policy Policy Gradient Methods Align?
Policy gradient methods are widely adopted reinforcement learning algorithms for tasks with continuous action spaces. These methods succeeded in many application domains, however, because of their notorious sample ineffi…
Policy Gradient MethodsIdentifying Policy Gradient Subspaces
Policy gradient methods hold great potential for solving complex continuous control tasks. Still, their training efficiency can be improved by exploiting structure within the optimization problem. Recent work indicates t…
continuous-controlContinuous ControlPolicy Gradient Methodsreinforcement-learning+1Global Convergence of Natural Policy Gradient with Hessian-aided Momentum Variance Reduction
Natural policy gradient (NPG) and its variants are widely-used policy search methods in reinforcement learning. Inspired by prior work, a new NPG variant coined NPG-HM is developed in this paper, which utilizes the Hessi…
MuJoCoPolicy Gradient MethodsTraining Diffusion Models Towards Diverse Image Generation with Reinforcement Learning
Diffusion models have demonstrated unprecedented capabilities in image generation. Yet they incorporate and amplify the data bias (e.g. gender age) from the original training set limiting the diversity of generated i…
Decision MakingDiversityImage GenerationPolicy Gradient Methods+2Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence Beyond the Minty Property
Policy gradient methods enjoy strong practical performance in numerous tasks in reinforcement learning. Their theoretical understanding in multiagent settings, however, remains limited, especially beyond two-player compe…
Policy Gradient MethodsPrivacy Preserving Multi-Agent Reinforcement Learning in Supply Chains
This paper addresses privacy concerns in multi-agent reinforcement learning (MARL), specifically within the context of supply chains where individual strategic data must remain confidential. Organizations within the supp…
Multi-agent Reinforcement LearningPolicy Gradient MethodsPrivacy Preservingreinforcement-learning+1