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 301325 of 15113 papers

TitleStatusHype
Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical RobotCode2
Constrained Decision Transformer for Offline Safe Reinforcement LearningCode2
Grounding Large Language Models in Interactive Environments with Online Reinforcement LearningCode2
Efficient Online Reinforcement Learning with Offline DataCode2
Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement LearningCode2
Heterogeneous Multi-Robot Reinforcement LearningCode2
Learning Physically Realizable Skills for Online Packing of General 3D ShapesCode2
MO-Gym: A Library of Multi-Objective Reinforcement Learning EnvironmentsCode2
Learning Heterogeneous Agent Cooperation via Multiagent League TrainingCode2
A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, ChallengesCode2
DIAMBRA Arena: a New Reinforcement Learning Platform for Research and ExperimentationCode2
Harfang3D Dog-Fight Sandbox: A Reinforcement Learning Research Platform for the Customized Control Tasks of Fighter AircraftsCode2
In-Hand Object Rotation via Rapid Motor AdaptationCode2
Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement LearningCode2
On Efficient Reinforcement Learning for Full-length Game of StarCraft IICode2
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement LearningCode2
Transformers are Sample-Efficient World ModelsCode2
A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement LearningCode2
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement LearningCode2
A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement LearningCode2
Deep Reinforcement Learning for Multi-Agent InteractionCode2
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement LearningCode2
DayDreamer: World Models for Physical Robot LearningCode2
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement LearningCode2
Challenges and Opportunities in Offline Reinforcement Learning from Visual ObservationsCode2
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Benchmark Results

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