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 1061–1070 of 15113 papers

TitleStatusHype
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Continual Model-Based Reinforcement Learning with HypernetworksCode1
Co-Reinforcement Learning for Unified Multimodal Understanding and GenerationCode1
A Distributional Perspective on Reinforcement LearningCode1
Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human PlayerCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy GamesCode1
CompoSuite: A Compositional Reinforcement Learning BenchmarkCode1
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Benchmark Results

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