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 4491–4500 of 15113 papers

TitleStatusHype
Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels—0
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces—0
A Language Model based Evaluator for Sentence Compression—0
A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning—0
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning—0
A Learned Simulation Environment to Model Plant Growth in Indoor Farming—0
A Learned Simulation Environment to Model Student Engagement and Retention in Automated Online Courses—0
A Learning based Branch and Bound for Maximum Common Subgraph Problems—0
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning—0
A Learning Framework for High Precision Industrial Assembly—0
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Benchmark Results

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