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 1561–1570 of 15113 papers

TitleStatusHype
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
Batch Exploration with Examples for Scalable Robotic Reinforcement LearningCode1
Diffusion Reward: Learning Rewards via Conditional Video DiffusionCode1
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement LearningCode1
Direct Behavior Specification via Constrained Reinforcement LearningCode1
Generalizable Visual Reinforcement Learning with Segment Anything ModelCode1
DisCor: Corrective Feedback in Reinforcement Learning via Distribution CorrectionCode1
Multi-Agent Environments for Vehicle Routing ProblemsCode1
Generalize a Small Pre-trained Model to Arbitrarily Large TSP InstancesCode1
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Benchmark Results

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