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 3491–3500 of 15113 papers

TitleStatusHype
Control invariant set enhanced reinforcement learning for process control: improved sampling efficiency and guaranteed stability—0
Optimal Interpretability-Performance Trade-off of Classification Trees with Black-Box Reinforcement Learning—0
Feudal Graph Reinforcement LearningCode0
RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUsCode1
Uncertainty-driven Trajectory Truncation for Data Augmentation in Offline Reinforcement LearningCode0
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal—0
Learning a Universal Human Prior for Dexterous Manipulation from Human Preference—0
Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ CamerasCode1
AI-Driven Resource Allocation in Optical Wireless Communication Systems—0
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic ApproachCode1
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Benchmark Results

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