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 921–930 of 15113 papers

TitleStatusHype
DISK: Learning local features with policy gradientCode1
Distilling Reinforcement Learning Tricks for Video GamesCode1
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
Converting Biomechanical Models from OpenSim to MuJoCoCode1
A Max-Min Entropy Framework for Reinforcement LearningCode1
GridMask Data AugmentationCode1
Distributional Reinforcement Learning via Moment MatchingCode1
Adversarial Deep Reinforcement Learning in Portfolio ManagementCode1
Guiding Online Reinforcement Learning with Action-Free Offline PretrainingCode1
Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving PoliciesCode1
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Benchmark Results

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