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 1761–1770 of 15113 papers

TitleStatusHype
Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational GraphCode1
Behavior From the Void: Unsupervised Active Pre-TrainingCode1
Explainable Reinforcement Learning for Longitudinal ControlCode1
RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement LearningCode1
Explainable Reinforcement Learning via a Causal World ModelCode1
Hierarchical clustering in particle physics through reinforcement learningCode1
Hierarchical Learning-based Graph Partition for Large-scale Vehicle Routing ProblemsCode1
Hearts Gym: Learning Reinforcement Learning as a Team EventCode1
BEAR: Physics-Principled Building Environment for Control and Reinforcement LearningCode1
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory WeightingCode1
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Benchmark Results

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