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 1771–1780 of 15113 papers

TitleStatusHype
Hierarchical clustering in particle physics through reinforcement learningCode1
Automatic Curriculum Learning through Value DisagreementCode1
Automatic Data Augmentation for Generalization in Deep Reinforcement LearningCode1
Automatic Data Augmentation for Generalization in Reinforcement LearningCode1
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise DatasetsCode1
Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion PlanningCode1
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement LearningCode1
Exploration by Random Network DistillationCode1
Exploration in Approximate Hyper-State Space for Meta Reinforcement LearningCode1
Hierarchical Learning-based Graph Partition for Large-scale Vehicle Routing ProblemsCode1
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Benchmark Results

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