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 201250 of 15113 papers

TitleStatusHype
A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement LearningCode2
High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement LearningCode2
Foundation Policies with Hilbert RepresentationsCode2
G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement LearningCode2
ARPO:End-to-End Policy Optimization for GUI Agents with Experience ReplayCode2
FlowReasoner: Reinforcing Query-Level Meta-AgentsCode2
Generalized Inner Loop Meta-LearningCode2
A Critical Evaluation of AI Feedback for Aligning Large Language ModelsCode2
Assessment of Reinforcement Learning for Macro PlacementCode2
Integrating Reinforcement Learning with Foundation Models for Autonomous Robotics: Methods and PerspectivesCode2
Flightmare: A Flexible Quadrotor SimulatorCode2
A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, ChallengesCode2
Jack of All Trades, Master of Some, a Multi-Purpose Transformer AgentCode2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement LearningCode2
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
Flow: A Modular Learning Framework for Mixed Autonomy TrafficCode2
Language Models can Solve Computer TasksCode2
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative FinanceCode2
Smooth Exploration for Robotic Reinforcement LearningCode2
Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based MethodsCode2
Learning Heterogeneous Agent Cooperation via Multiagent League TrainingCode2
Feedback Efficient Online Fine-Tuning of Diffusion ModelsCode2
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein DesignCode2
AndroidEnv: A Reinforcement Learning Platform for AndroidCode2
Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1Code2
EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and BenchmarkingCode2
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path PlanningCode2
LongReward: Improving Long-context Large Language Models with AI FeedbackCode2
Evolving Reservoirs for Meta Reinforcement LearningCode2
Exploring the Limit of Outcome Reward for Learning Mathematical ReasoningCode2
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function OptimizationCode2
AMP: Adversarial Motion Priors for Stylized Physics-Based Character ControlCode2
Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language ModelsCode2
A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement LearningCode2
RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous ControlCode2
Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language ModelsCode2
A Comparative Study of Algorithms for Intelligent Traffic Signal ControlCode2
Emergent Tool Use From Multi-Agent AutocurriculaCode2
Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement LearningCode2
Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy OptimizationCode2
Model-agnostic and Scalable Counterfactual Explanations via Reinforcement LearningCode2
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode2
moolib: A Platform for Distributed RLCode2
Efficient World Models with Context-Aware TokenizationCode2
Multi-Agent Reinforcement Learning is a Sequence Modeling ProblemCode2
EfficientZero V2: Mastering Discrete and Continuous Control with Limited DataCode2
Natural Language Reinforcement LearningCode2
Benchmarking Deep Reinforcement Learning for Continuous ControlCode2
DexGarmentLab: Dexterous Garment Manipulation Environment with Generalizable PolicyCode2
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Benchmark Results

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