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 3581–3590 of 15113 papers

TitleStatusHype
Reinforcement Learning for Rate Maximization in IRS-aided OWC Networks—0
Reward Guidance for Reinforcement Learning Tasks Based on Large Language Models: The LMGT Framework—0
Sample and Oracle Efficient Reinforcement Learning for MDPs with Linearly-Realizable Value Functions—0
Causality-Driven Reinforcement Learning for Joint Communication and Sensing—0
Gaussian-Mixture-Model Q-Functions for Reinforcement Learning by Riemannian Optimization—0
Robust synchronization and policy adaptation for networked heterogeneous agents—0
InfraLib: Enabling Reinforcement Learning and Decision-Making for Large-Scale Infrastructure Management—0
Reinforcement Learning Approach to Optimizing Profilometric Sensor Trajectories for Surface Inspection—0
CHIRPs: Change-Induced Regret Proxy metrics for Lifelong Reinforcement Learning—0
Differentiable Discrete Event Simulation for Queuing Network Control—0
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Benchmark Results

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