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 2191–2200 of 15113 papers

TitleStatusHype
Automated Discovery of Functional Actual Causes in Complex Environments—0
What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement LearningCode0
Achieving Constant Regret in Linear Markov Decision Processes—0
Offline Trajectory Generalization for Offline Reinforcement Learning—0
Simplex Decomposition for Portfolio Allocation Constraints in Reinforcement Learning—0
The Feasibility of Constrained Reinforcement Learning Algorithms: A Tutorial Study—0
Autonomous Path Planning for Intercostal Robotic Ultrasound Imaging Using Reinforcement Learning—0
Effective Reinforcement Learning Based on Structural Information Principles—0
Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement LearningCode0
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning—0
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Benchmark Results

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