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 3901–3910 of 15113 papers

TitleStatusHype
Identifying Expert Behavior in Offline Training Datasets Improves Behavioral Cloning of Robotic Manipulation PoliciesCode0
V2N Service Scaling with Deep Reinforcement Learning—0
PAC-Bayesian Soft Actor-Critic LearningCode0
Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation—0
Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents—0
Guiding Online Reinforcement Learning with Action-Free Offline PretrainingCode1
Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs—0
Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem—0
Autonomous Satellite Docking via Adaptive Optimal Output Rregulation: A Reinforcement Learning Approach—0
A Deep Reinforcement Learning Framework for Optimizing Congestion Control in Data Centers—0
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Benchmark Results

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