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 4011–4020 of 15113 papers

TitleStatusHype
Dexterous Manipulation through Imitation Learning: A Survey—0
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost—0
Coordinated Multi-Agent Exploration Using Shared Goals—0
Coordinated Frequency Control through Safe Reinforcement Learning—0
Assessing Policy, Loss and Planning Combinations in Reinforcement Learning using a New Modular Architecture—0
Bayesian policy selection using active inference—0
A Generalized Natural Actor-Critic Algorithm—0
Coordinated Exploration in Concurrent Reinforcement Learning—0
Diagnosing Reinforcement Learning for Traffic Signal Control—0
Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study—0
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Benchmark Results

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