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 3101–3110 of 15113 papers

TitleStatusHype
AdapShare: An RL-Based Dynamic Spectrum Sharing Solution for O-RAN—0
Overcoming Catastrophic Interference in Online Reinforcement Learning with Dynamic Self-Organizing Maps—0
A Survey of Knowledge-based Sequential Decision Making under Uncertainty—0
A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress—0
AdaPool: A Diurnal-Adaptive Fleet Management Framework using Model-Free Deep Reinforcement Learning and Change Point Detection—0
A Survey of In-Context Reinforcement Learning—0
A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges—0
Deep Reinforcement Learning amidst Lifelong Non-Stationarity—0
Cross-Embodiment Dexterous Grasping with Reinforcement Learning—0
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning—0
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Benchmark Results

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