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 3591–3600 of 15113 papers

TitleStatusHype
Robust synchronization and policy adaptation for networked heterogeneous agents—0
Continual Diffuser (CoD): Mastering Continual Offline Reinforcement Learning with Experience RehearsalCode0
Large Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement Learning—0
Enhancing Information Freshness: An AoI Optimized Markov Decision Process Dedicated In the Underwater Task—0
Tractable Offline Learning of Regular Decision Processes—0
State and Action Factorization in Power Grids—0
Reinforcement Learning-enabled Satellite Constellation Reconfiguration and Retasking for Mission-Critical Applications—0
MOOSS: Mask-Enhanced Temporal Contrastive Learning for Smooth State Evolution in Visual Reinforcement LearningCode0
Grounding Language Models in Autonomous Loco-manipulation Tasks—0
AgGym: An agricultural biotic stress simulation environment for ultra-precision management planningCode0
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Benchmark Results

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