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 2471–2480 of 15113 papers

TitleStatusHype
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement LearningCode3
Vision-Language Models Provide Promptable Representations for Reinforcement Learning—0
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate—0
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short DelaysCode0
Replication of Impedance Identification Experiments on a Reinforcement-Learning-Controlled Digital Twin of Human ElbowsCode0
Understanding What Affects the Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence—0
The Virtues of Pessimism in Inverse Reinforcement Learning—0
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching—0
A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control—0
Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach—0
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Benchmark Results

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