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 4051–4060 of 15113 papers

TitleStatusHype
What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement LearningCode0
Offline Trajectory Generalization for Offline Reinforcement Learning—0
Course Recommender Systems Need to Consider the Job MarketCode0
Automated Discovery of Functional Actual Causes in Complex Environments—0
Effective Reinforcement Learning Based on Structural Information Principles—0
Autonomous Path Planning for Intercostal Robotic Ultrasound Imaging Using Reinforcement Learning—0
Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement LearningCode0
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning—0
The Feasibility of Constrained Reinforcement Learning Algorithms: A Tutorial Study—0
Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts—0
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Benchmark Results

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