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 3861–3870 of 15113 papers

TitleStatusHype
Mind the Gap: Offline Policy Optimization for Imperfect RewardsCode1
Deep Reinforcement Learning for Online Error Detection in Cyber-Physical Systems—0
Deep Reinforcement Learning for Cyber System Defense under Dynamic Adversarial Uncertainties—0
Distributional constrained reinforcement learning for supply chain optimizationCode0
Learning to Optimize for Reinforcement LearningCode1
Two-Stage Constrained Actor-Critic for Short Video RecommendationCode1
Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms—0
Lower Bounds for Learning in Revealing POMDPs—0
ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints—0
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition—0
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Benchmark Results

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