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

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Showing 881–890 of 15113 papers

TitleStatusHype
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement LearningCode1
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation TasksCode1
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial GamesCode1
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline GenerationCode1
Aligning Language Models with Human Preferences via a Bayesian ApproachCode1
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise DatasetsCode1
Constrained Policy Optimization via Bayesian World ModelsCode1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement LearningCode1
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Benchmark Results

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