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 2231–2240 of 15113 papers

TitleStatusHype
A Crash Course on Reinforcement LearningCode1
Constrained Policy Optimization via Bayesian World ModelsCode1
An Experimental Design Perspective on Model-Based Reinforcement LearningCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
Effective and Transparent RAG: Adaptive-Reward Reinforcement Learning for Decision TraceabilityCode1
Effective Diversity in Population Based Reinforcement LearningCode1
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted PrescriptionCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
DataLight: Offline Data-Driven Traffic Signal ControlCode1
Imitating Graph-Based Planning with Goal-Conditioned PoliciesCode1
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Benchmark Results

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