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 Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement LearningCode2
Habitat 2.0: Training Home Assistants to Rearrange their HabitatCode2
High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement LearningCode2
Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning BenchmarksCode2
Flow: A Modular Learning Framework for Mixed Autonomy TrafficCode2
FlowReasoner: Reinforcing Query-Level Meta-AgentsCode2
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative FinanceCode2
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein DesignCode2
In-Hand Object Rotation via Rapid Motor AdaptationCode2
Integrating Reinforcement Learning with Foundation Models for Autonomous Robotics: Methods and PerspectivesCode2
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement LearningCode2
Assessment of Reinforcement Learning for Macro PlacementCode2
Foundation Policies with Hilbert RepresentationsCode2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMsCode2
Exploring the Limit of Outcome Reward for Learning Mathematical ReasoningCode2
ARPO:End-to-End Policy Optimization for GUI Agents with Experience ReplayCode2
Language Models can Solve Computer TasksCode2
Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1Code2
Feedback Efficient Online Fine-Tuning of Diffusion ModelsCode2
Learning Heterogeneous Agent Cooperation via Multiagent League TrainingCode2
Learning Physically Realizable Skills for Online Packing of General 3D ShapesCode2
A Review of Safe Reinforcement Learning: Methods, Theory and ApplicationsCode2
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
AGILE: A Novel Reinforcement Learning Framework of LLM AgentsCode2
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path PlanningCode2
LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban EnvironmentsCode2
EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and BenchmarkingCode2
Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy OptimizationCode2
Evolving Reservoirs for Meta Reinforcement LearningCode2
Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based MethodsCode2
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex ManipulationCode2
Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement LearningCode2
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement LearningCode2
MBRL-Lib: A Modular Library for Model-based Reinforcement LearningCode2
Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement LearningCode2
A Comparative Study of Algorithms for Intelligent Traffic Signal ControlCode2
Emergent Tool Use From Multi-Agent AutocurriculaCode2
Efficient World Models with Context-Aware TokenizationCode2
Model-agnostic and Scalable Counterfactual Explanations via Reinforcement LearningCode2
MO-Gym: A Library of Multi-Objective Reinforcement Learning EnvironmentsCode2
Efficient Online Reinforcement Learning with Offline DataCode2
EfficientZero V2: Mastering Discrete and Continuous Control with Limited DataCode2
Multi-Agent Reinforcement Learning is a Sequence Modeling ProblemCode2
GenRL: Multimodal-foundation world models for generalization in embodied agentsCode2
EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender SystemsCode2
Aligning AI With Shared Human ValuesCode2
Benchmarking Deep Reinforcement Learning for Continuous ControlCode2
Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionCode2
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Benchmark Results

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