SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1701–1725 of 15113 papers

TitleStatusHype
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation TasksCode1
Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous DrivingCode1
Energy Pricing in P2P Energy Systems Using Reinforcement LearningCode1
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum GamesCode1
PlanGAN: Model-based Planning With Sparse Rewards and Multiple GoalsCode1
Planning to Explore via Self-Supervised World ModelsCode1
Energy Harvesting Reconfigurable Intelligent Surface for UAV Based on Robust Deep Reinforcement LearningCode1
Energy-Based Imitation LearningCode1
Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement LearningCode1
Harnessing Discrete Representations For Continual Reinforcement LearningCode1
An Empirical Study of Representation Learning for Reinforcement Learning in HealthcareCode1
Gym-μRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement LearningCode1
A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics TasksCode1
Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward EnvironmentsCode1
Enhancing Efficiency and Exploration in Reinforcement Learning for LLMsCode1
Enhancing data efficiency in reinforcement learning: a novel imagination mechanism based on mesh information propagationCode1
An empirical investigation of the challenges of real-world reinforcement learningCode1
Policy Expansion for Bridging Offline-to-Online Reinforcement LearningCode1
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGymCode1
Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical InvestigationCode1
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentsCode1
Large Language Models are Learnable Planners for Long-Term RecommendationCode1
Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based NavigationCode1
Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution SystemsCode1
Harnessing Equivariance: Modeling Turbulence with Graph Neural NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified