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 721–730 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Process SynthesisCode1
Zero-Shot Compositional Policy Learning via Language GroundingCode1
Active MR k-space Sampling with Reinforcement LearningCode1
Deep Reinforcement Learning for Producing Furniture Layout in Indoor ScenesCode1
Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution SystemsCode1
Deep Reinforcement Learning for List-wise RecommendationsCode1
Deep reinforcement learning for large-scale epidemic controlCode1
Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book ModelCode1
Adversarially Trained Actor Critic for Offline Reinforcement LearningCode1
Deep Reinforcement Learning for Resource Allocation in Business ProcessesCode1
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Benchmark Results

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