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 311320 of 15113 papers

TitleStatusHype
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
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Benchmark Results

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