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 151200 of 15113 papers

TitleStatusHype
Integrating Reinforcement Learning with Foundation Models for Autonomous Robotics: Methods and PerspectivesCode2
Interactive Differentiable SimulationCode2
Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement LearningCode2
In-Hand Object Rotation via Rapid Motor AdaptationCode2
InterCode: Standardizing and Benchmarking Interactive Coding with Execution FeedbackCode2
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAXCode2
Learning to Fly in SecondsCode2
A Review of Safe Reinforcement Learning: Methods, Theory and ApplicationsCode2
Human-AI Shared Control via Policy DissectionCode2
Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning BenchmarksCode2
High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement LearningCode2
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement LearningCode2
Habitat 2.0: Training Home Assistants to Rearrange their HabitatCode2
Harfang3D Dog-Fight Sandbox: A Reinforcement Learning Research Platform for the Customized Control Tasks of Fighter AircraftsCode2
GTA1: GUI Test-time Scaling AgentCode2
Grounding Large Language Models in Interactive Environments with Online Reinforcement LearningCode2
Guiding Generative Protein Language Models with Reinforcement LearningCode2
Heterogeneous Multi-Robot Reinforcement LearningCode2
HumanOmniV2: From Understanding to Omni-Modal Reasoning with ContextCode2
GPG: A Simple and Strong Reinforcement Learning Baseline for Model ReasoningCode2
Godot Reinforcement Learning AgentsCode2
Gradient Boosting Reinforcement LearningCode2
Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and MemoryCode2
Smooth Exploration for Robotic Reinforcement LearningCode2
Generalized Inner Loop Meta-LearningCode2
Generative Auto-Bidding with Value-Guided ExplorationsCode2
FlowReasoner: Reinforcing Query-Level Meta-AgentsCode2
ARPO:End-to-End Policy Optimization for GUI Agents with Experience ReplayCode2
Foundation Policies with Hilbert RepresentationsCode2
GenNBV: Generalizable Next-Best-View Policy for Active 3D ReconstructionCode2
Graphs Meet AI Agents: Taxonomy, Progress, and Future OpportunitiesCode2
iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvementCode2
Learning to Predict Without Looking Ahead: World Models Without Forward PredictionCode2
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein DesignCode2
Feedback Efficient Online Fine-Tuning of Diffusion ModelsCode2
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
Exploring the Limit of Outcome Reward for Learning Mathematical ReasoningCode2
Evolving Reservoirs for Meta Reinforcement LearningCode2
AndroidEnv: A Reinforcement Learning Platform for AndroidCode2
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative FinanceCode2
Flow: A Modular Learning Framework for Mixed Autonomy TrafficCode2
Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy OptimizationCode2
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function OptimizationCode2
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex ManipulationCode2
G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement LearningCode2
Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language ModelsCode2
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path PlanningCode2
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement LearningCode2
AMP: Adversarial Motion Priors for Stylized Physics-Based Character ControlCode2
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Benchmark Results

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