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 2601–2610 of 15113 papers

TitleStatusHype
Adaptive Neural Architectures for Recommender Systems—0
ABC Reinforcement Learning—0
Autonomous particles—0
Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning—0
A Machine of Few Words -- Interactive Speaker Recognition with Reinforcement Learning—0
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition—0
A Machine Learning Approach to Routing—0
Reinforcement Learning with Non-Markovian Rewards—0
Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governance—0
Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication—0
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Benchmark Results

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