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 3241–3250 of 15113 papers

TitleStatusHype
Data-Efficient Exploration with Self Play for Atari—0
Asynchronous training of quantum reinforcement learning—0
Data-Efficient Learning from Human Interventions for Mobile Robots—0
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models—0
A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks—0
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data—0
Data-Efficient Quadratic Q-Learning Using LMIs—0
Data efficient reinforcement learning and adaptive optimal perimeter control of network traffic dynamics—0
Data Efficient Reinforcement Learning for Legged Robots—0
Creativity in Robot Manipulation with Deep Reinforcement Learning—0
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Benchmark Results

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