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

Showing 19912000 of 15113 papers

TitleStatusHype
Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward DecompositionCode1
CURL: Contrastive Unsupervised Representations for Reinforcement LearningCode1
An Application of Deep Reinforcement Learning to Algorithmic TradingCode1
MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement LearningCode1
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement LearningCode1
Action Space Shaping in Deep Reinforcement LearningCode1
Multi-Task Reinforcement Learning with Soft ModularizationCode1
Agent57: Outperforming the Atari Human BenchmarkCode1
Deep reinforcement learning for large-scale epidemic controlCode1
Ultrasound-Guided Robotic Navigation with Deep Reinforcement LearningCode1
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Benchmark Results

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