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Q-Learning

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

( Image credit: Playing Atari with Deep Reinforcement Learning )

Papers

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Showing 961–970 of 1918 papers

TitleStatusHype
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning—0
Event-Based Communication in Distributed Q-Learning—0
Efficient Drone Mobility Support Using Reinforcement Learning—0
Efficient LSTM Training with Eligibility Traces—0
Efficient Off-Policy Q-Learning for Data-Based Discrete-Time LQR Problems—0
Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery—0
Logit-Q Dynamics for Efficient Learning in Stochastic Teams—0
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning—0
Efficient Triangular Arbitrage Detection via Graph Neural Networks—0
Elastic Decision Transformer—0
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