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 2121–2130 of 15113 papers

TitleStatusHype
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement LearningCode1
An Optimistic Perspective on Offline Reinforcement LearningCode1
Multi-Agent Deep Reinforcement Learning for Liquidation Strategy AnalysisCode1
Reinforcement Learning with Convex ConstraintsCode1
A Story of Two Streams: Reinforcement Learning Models from Human Behavior and NeuropsychiatryCode1
Split Q Learning: Reinforcement Learning with Two-Stream RewardsCode1
Unsupervised Learning of Object Keypoints for Perception and ControlCode1
When to Trust Your Model: Model-Based Policy OptimizationCode1
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement LearningCode1
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Benchmark Results

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