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 3631–3640 of 15113 papers

TitleStatusHype
Learning Bipedal Walking for Humanoids with Current FeedbackCode3
On the Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples—0
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement LearningCode1
Dexterous In-hand Manipulation by Guiding Exploration with Simple Sub-skill Controllers—0
Perspectives on the Social Impacts of Reinforcement Learning with Human Feedback—0
Reinforcement Learning Based Self-play and State Stacking Techniques for Noisy Air Combat Environment—0
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning—0
Safe Reinforcement Learning via Probabilistic Logic ShieldsCode0
Efficient Skill Acquisition for Complex Manipulation Tasks in Obstructed Environments—0
Ensemble Reinforcement Learning: A Survey—0
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Benchmark Results

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