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 2681–2690 of 15113 papers

TitleStatusHype
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning—0
Cross-Domain Transfer via Semantic Skill Imitation—0
CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection—0
CycLight: learning traffic signal cooperation with a cycle-level strategy—0
Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition—0
Centerline Depth World Reinforcement Learning-based Left Atrial Appendage Orifice Localization—0
Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing—0
Automatic Goal Generation using Dynamical Distance Learning—0
Automatic Goal Generation using Dynamical Distance Learning—0
Adaptive Honeypot Engagement through Reinforcement Learning of Semi-Markov Decision Processes—0
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Benchmark Results

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