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 1021–1030 of 15113 papers

TitleStatusHype
Automating DBSCAN via Deep Reinforcement LearningCode1
Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement LearningCode1
Relay Hindsight Experience Replay: Self-Guided Continual Reinforcement Learning for Sequential Object Manipulation Tasks with Sparse RewardsCode1
Model-based graph reinforcement learning for inductive traffic signal controlCode1
Performance Comparison of Deep RL Algorithms for Energy Systems Optimal SchedulingCode1
Unified Automatic Control of Vehicular Systems with Reinforcement LearningCode1
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
Lifelong Machine Learning of Functionally Compositional StructuresCode1
Learning Soccer Juggling Skills with Layer-wise Mixture-of-ExpertsCode1
Hierarchical Kickstarting for Skill Transfer in Reinforcement LearningCode1
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Benchmark Results

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