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 1771–1780 of 15113 papers

TitleStatusHype
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information CollaborationCode1
Automatic Curriculum Learning through Value DisagreementCode1
Automatic Data Augmentation for Generalization in Deep Reinforcement LearningCode1
Automatic Data Augmentation for Generalization in Reinforcement LearningCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Policy Gradient RL Algorithms as Directed Acyclic GraphsCode1
Policy Learning for Off-Dynamics RL with Deficient SupportCode1
Policy Regularization with Dataset Constraint for Offline Reinforcement LearningCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
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Benchmark Results

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