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 3051–3060 of 15113 papers

TitleStatusHype
A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networks—0
A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility—0
Adapting Auxiliary Losses Using Gradient Similarity—0
A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning—0
A Graph Attention Learning Approach to Antenna Tilt Optimization—0
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model—0
A Survey on Model-based Reinforcement Learning—0
A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning—0
AGPNet -- Autonomous Grading Policy Network—0
ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning—0
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Benchmark Results

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