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 351–360 of 15113 papers

TitleStatusHype
Brax -- A Differentiable Physics Engine for Large Scale Rigid Body SimulationCode2
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement LearningCode2
Model-agnostic and Scalable Counterfactual Explanations via Reinforcement LearningCode2
AndroidEnv: A Reinforcement Learning Platform for AndroidCode2
MBRL-Lib: A Modular Library for Model-based Reinforcement LearningCode2
AMP: Adversarial Motion Priors for Stylized Physics-Based Character ControlCode2
Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter ControlCode2
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph MatchingCode2
Learning Accurate Long-term Dynamics for Model-based Reinforcement LearningCode2
Connections between Relational Event Model and Inverse Reinforcement Learning for Characterizing Group Interaction SequencesCode2
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Benchmark Results

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