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 1671–1680 of 15113 papers

TitleStatusHype
Learning Multi-Agent Communication through Structured Attentive ReasoningCode1
Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and AlgorithmsCode1
Self-supervised Visual Reinforcement Learning with Object-centric RepresentationsCode1
Generalization in Reinforcement Learning by Soft Data AugmentationCode1
Optimization of the Model Predictive Control Update Interval Using Reinforcement LearningCode1
Interactive Machine Learning of Musical GestureCode1
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Symmetry-Aware Actor-Critic for 3D Molecular DesignCode1
World Model as a Graph: Learning Latent Landmarks for PlanningCode1
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Benchmark Results

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