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 711–720 of 15113 papers

TitleStatusHype
Active MR k-space Sampling with Reinforcement LearningCode1
Deep reinforcement learning for large-scale epidemic controlCode1
Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book ModelCode1
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
Deep Reinforcement Learning for Conservation DecisionsCode1
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement LearningCode1
AutoPhoto: Aesthetic Photo Capture using Reinforcement LearningCode1
DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement LearningCode1
Deep Reinforcement Learning for Computational Fluid Dynamics on HPC SystemsCode1
Deep Reinforcement Learning for Cost-Effective Medical DiagnosisCode1
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Benchmark Results

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