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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 591–600 of 1918 papers

TitleStatusHype
Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling—0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning—0
Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation—0
Directed Exploration in PAC Model-Free Reinforcement Learning—0
Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach—0
Balancing Two-Player Stochastic Games with Soft Q-Learning—0
A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategies—0
Double Deep Q-Learning in Opponent Modeling—0
Density Estimation for Conservative Q-Learning—0
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants—0
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