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
Harfang3D Dog-Fight Sandbox: A Reinforcement Learning Research Platform for the Customized Control Tasks of Fighter AircraftsCode2
Heterogeneous Multi-Robot Reinforcement LearningCode2
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement LearningCode2
Flightmare: A Flexible Quadrotor SimulatorCode2
Foundation Policies with Hilbert RepresentationsCode2
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative FinanceCode2
Assessment of Reinforcement Learning for Macro PlacementCode2
Advancing Language Model Reasoning through Reinforcement Learning and Inference ScalingCode2
Interactive Differentiable SimulationCode2
InterCode: Standardizing and Benchmarking Interactive Coding with Execution FeedbackCode2
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex ManipulationCode2
Feedback Efficient Online Fine-Tuning of Diffusion ModelsCode2
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAXCode2
Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based MethodsCode2
Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1Code2
LLMLight: Large Language Models as Traffic Signal Control AgentsCode2
Agent models: Internalizing Chain-of-Action Generation into Reasoning modelsCode2
ARPO:End-to-End Policy Optimization for GUI Agents with Experience ReplayCode2
Exploring the Limit of Outcome Reward for Learning Mathematical ReasoningCode2
Learning Physically Realizable Skills for Online Packing of General 3D ShapesCode2
Learning through Dialogue Interactions by Asking QuestionsCode2
A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement LearningCode2
Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest QuestionsCode2
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein DesignCode2
AGILE: A Novel Reinforcement Learning Framework of LLM AgentsCode2
Evolving Reservoirs for Meta Reinforcement LearningCode2
A Review of Safe Reinforcement Learning: Methods, Theory and ApplicationsCode2
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path PlanningCode2
EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and BenchmarkingCode2
G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement LearningCode2
Emergent Tool Use From Multi-Agent AutocurriculaCode2
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function OptimizationCode2
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement LearningCode2
Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language ModelsCode2
Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement LearningCode2
EfficientZero V2: Mastering Discrete and Continuous Control with Limited DataCode2
A Comparative Study of Algorithms for Intelligent Traffic Signal ControlCode2
Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement LearningCode2
Model-agnostic and Scalable Counterfactual Explanations via Reinforcement LearningCode2
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement LearningCode2
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode2
Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language ModelsCode2
Aligning AI With Shared Human ValuesCode2
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline DataCode2
Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model LearningCode2
Benchmarking Deep Reinforcement Learning for Continuous ControlCode2
Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionCode2
Benchmarking Potential Based Rewards for Learning Humanoid LocomotionCode2
Efficient Online Reinforcement Learning with Offline DataCode2
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Benchmark Results

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