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 1431–1440 of 15113 papers

TitleStatusHype
Learning to Act without ActionsCode1
Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise SafetyCode1
Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile NetworksCode1
Deep Active Inference for Partially Observable MDPsCode1
Fault-Tolerant Federated Reinforcement Learning with Theoretical GuaranteeCode1
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement LearningCode1
Fast Template Matching and Update for Video Object Tracking and SegmentationCode1
Deep Black-Box Reinforcement Learning with Movement PrimitivesCode1
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink PaintingCode1
Feasibility Consistent Representation Learning for Safe Reinforcement LearningCode1
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Benchmark Results

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