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

Showing 101125 of 15113 papers

TitleStatusHype
Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRsCode3
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning LibraryCode3
Adversarial Cheap TalkCode3
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning AlgorithmsCode3
Deep Reinforcement LearningCode3
CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character controlCode3
Learning to Reason under Off-Policy GuidanceCode3
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and PlanningCode3
OpenSpiel: A Framework for Reinforcement Learning in GamesCode3
Rainbow: Combining Improvements in Deep Reinforcement LearningCode3
ACEGEN: Reinforcement learning of generative chemical agents for drug discoveryCode3
Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid AlgorithmsCode3
Is Value Learning Really the Main Bottleneck in Offline RL?Code3
imitation: Clean Imitation Learning ImplementationsCode3
CarDreamer: Open-Source Learning Platform for World Model based Autonomous DrivingCode3
Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement LearningCode3
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise RewardCode3
Learning Bipedal Walking for Humanoids with Current FeedbackCode3
Flow Q-LearningCode3
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative FinanceCode3
General-Reasoner: Advancing LLM Reasoning Across All DomainsCode3
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RLCode3
A Clean Slate for Offline Reinforcement LearningCode3
ExTrans: Multilingual Deep Reasoning Translation via Exemplar-Enhanced Reinforcement LearningCode3
Accelerating Goal-Conditioned RL Algorithms and ResearchCode3
Show:102550
← PrevPage 5 of 605Next →

Benchmark Results

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