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 3431–3440 of 15113 papers

TitleStatusHype
Federated Ensemble-Directed Offline Reinforcement LearningCode1
Explainable Reinforcement Learning via a Causal World ModelCode1
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory—0
Rethinking Population-assisted Off-policy Reinforcement Learning—0
Toward Evaluating Robustness of Reinforcement Learning with Adversarial PolicyCode0
Simple Noisy Environment Augmentation for Reinforcement LearningCode0
Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender SystemsCode0
Gym-preCICE: Reinforcement Learning Environments for Active Flow Control—0
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in HealthcareCode1
Sample Efficient Model-free Reinforcement Learning from LTL Specifications with Optimality GuaranteesCode0
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Benchmark Results

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