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 2181–2190 of 15113 papers

TitleStatusHype
Reward Constrained Policy OptimizationCode1
Deep Reinforcement Learning For Sequence to Sequence ModelsCode1
Verifiable Reinforcement Learning via Policy ExtractionCode1
Reinforcement Learning and Control as Probabilistic Inference: Tutorial and ReviewCode1
Toward Diverse Text Generation with Inverse Reinforcement LearningCode1
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character SkillsCode1
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode1
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement LearningCode1
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement LearningCode1
World ModelsCode1
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Benchmark Results

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