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 4101–4110 of 15113 papers

TitleStatusHype
NARS vs. Reinforcement learning: ONA vs. Q-LearningCode0
Investigation of reinforcement learning for shape optimization of profile extrusion dies—0
A Learned Simulation Environment to Model Student Engagement and Retention in Automated Online Courses—0
Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement LearningCode0
Decoding surface codes with deep reinforcement learning and probabilistic policy reuse—0
Reinforcement Learning Based Approaches to Adaptive Context Caching in Distributed Context Management Systems—0
Robust Path Selection in Software-defined WANs using Deep Reinforcement Learning—0
On Reinforcement Learning for the Game of 2048Code1
Neighboring state-based RL Exploration—0
Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence SummarizationCode1
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Benchmark Results

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