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 1831–1840 of 15113 papers

TitleStatusHype
Multi-Step Reinforcement Learning for Single Image Super-ResolutionCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Monte-Carlo Tree Search as Regularized Policy OptimizationCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
Distributional Reinforcement Learning via Moment MatchingCode1
Value-Decomposition Multi-Agent Actor-CriticsCode1
BabyAI 1.1Code1
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued PoliciesCode1
Maximum Mutation Reinforcement Learning for Scalable ControlCode1
Integrating Deep Reinforcement Learning Networks with Health System SimulationsCode1
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Benchmark Results

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