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 101–150 of 15113 papers

TitleStatusHype
Accelerating Goal-Conditioned RL Algorithms and ResearchCode3
Reinforcement Learning Meets Visual OdometryCode3
Simplifying Deep Temporal Difference LearningCode3
Is Value Learning Really the Main Bottleneck in Offline RL?Code3
CarDreamer: Open-Source Learning Platform for World Model based Autonomous DrivingCode3
ACEGEN: Reinforcement learning of generative chemical agents for drug discoveryCode3
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RLCode3
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement LearningCode3
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement LearningCode3
Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid AlgorithmsCode3
Generating Synergistic Formulaic Alpha Collections via Reinforcement LearningCode3
OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning ResearchCode3
Learning Bipedal Walking for Humanoids with Current FeedbackCode3
EvoTorch: Scalable Evolutionary Computation in PythonCode3
Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement LearningCode3
imitation: Clean Imitation Learning ImplementationsCode3
Adversarial Cheap TalkCode3
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and PlanningCode3
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning LibraryCode3
Discovered Policy OptimisationCode3
Learning Bipedal Walking On Planned Footsteps For Humanoid RobotsCode3
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online VideosCode3
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning AlgorithmsCode3
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement LearningCode3
Tianshou: a Highly Modularized Deep Reinforcement Learning LibraryCode3
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative FinanceCode3
Fine-Tuning Language Models from Human PreferencesCode3
OpenSpiel: A Framework for Reinforcement Learning in GamesCode3
Dopamine: A Research Framework for Deep Reinforcement LearningCode3
Practical Deep Reinforcement Learning Approach for Stock TradingCode3
Deep Reinforcement LearningCode3
Distributed Prioritized Experience ReplayCode3
Rainbow: Combining Improvements in Deep Reinforcement LearningCode3
GTA1: GUI Test-time Scaling AgentCode2
High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement LearningCode2
AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMsCode2
Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement LearningCode2
HumanOmniV2: From Understanding to Omni-Modal Reasoning with ContextCode2
OctoThinker: Mid-training Incentivizes Reinforcement Learning ScalingCode2
Confucius3-Math: A Lightweight High-Performance Reasoning LLM for Chinese K-12 Mathematics LearningCode2
Graphs Meet AI Agents: Taxonomy, Progress, and Future OpportunitiesCode2
TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement LearningCode2
Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language ModelsCode2
TreeRL: LLM Reinforcement Learning with On-Policy Tree SearchCode2
Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement LearningCode2
Play to Generalize: Learning to Reason Through Game PlayCode2
Thinking vs. Doing: Agents that Reason by Scaling Test-Time InteractionCode2
Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest QuestionsCode2
Reasoning-Table: Exploring Reinforcement Learning for Table ReasoningCode2
ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RLCode2
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Benchmark Results

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