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 1631–1640 of 15113 papers

TitleStatusHype
ELO-Rated Sequence Rewards: Advancing Reinforcement Learning ModelsCode0
Reinforcement Learning Approach to Optimizing Profilometric Sensor Trajectories for Surface Inspection—0
Enhancing Information Freshness: An AoI Optimized Markov Decision Process Dedicated In the Underwater Task—0
Tractable Offline Learning of Regular Decision Processes—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
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
Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy OptimizationCode2
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Benchmark Results

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