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 1951–1975 of 15113 papers

TitleStatusHype
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-TuningCode1
SEER: Facilitating Structured Reasoning and Explanation via Reinforcement LearningCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-CriticCode1
CaiRL: A High-Performance Reinforcement Learning Environment ToolkitCode1
Self-critical Sequence Training for Image CaptioningCode1
Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification AlgorithmsCode1
Self-Paced Contextual Reinforcement LearningCode1
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement LearningCode1
Self-supervised Visual Reinforcement Learning with Object-centric RepresentationsCode1
Semiconductor Fab Scheduling with Self-Supervised and Reinforcement LearningCode1
Semi-Supervised Offline Reinforcement Learning with Action-Free TrajectoriesCode1
Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement LearningCode1
Sequential Planning in Large Partially Observable Environments guided by LLMsCode1
CommonPower: A Framework for Safe Data-Driven Smart Grid ControlCode1
Settling the Variance of Multi-Agent Policy GradientsCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Shortest-Path Constrained Reinforcement Learning for Sparse Reward TasksCode1
Basis for Intentions: Efficient Inverse Reinforcement Learning using Past ExperienceCode1
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
Conditional Mutual Information for Disentangled Representations in Reinforcement LearningCode1
Simple random search provides a competitive approach to reinforcement learningCode1
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement LearningCode1
Batch Exploration with Examples for Scalable Robotic Reinforcement LearningCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
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Benchmark Results

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