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 3111–3120 of 15113 papers

TitleStatusHype
Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration—0
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT—0
Deep Reinforcement Learning Attention Selection for Person Re-Identification—0
Cross-Embodiment Dexterous Grasping with Reinforcement Learning—0
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning—0
Cross-Domain Transfer via Semantic Skill Imitation—0
Cross-Domain Transfer in Reinforcement Learning using Target Apprentice—0
A Survey of Forex and Stock Price Prediction Using Deep Learning—0
CrossNorm: On Normalization for Off-Policy Reinforcement Learning—0
A Survey of Exploration Methods in Reinforcement Learning—0
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Benchmark Results

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