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 561–570 of 15113 papers

TitleStatusHype
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
Continual Model-Based Reinforcement Learning with HypernetworksCode1
Continual Learning with Gated Incremental Memories for sequential data processingCode1
Continual Reinforcement Learning with Multi-Timescale ReplayCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Contextualize Me -- The Case for Context in Reinforcement LearningCode1
Continual Backprop: Stochastic Gradient Descent with Persistent RandomnessCode1
Continual World: A Robotic Benchmark For Continual Reinforcement LearningCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
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Benchmark Results

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