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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 1801–1810 of 1918 papers

TitleStatusHype
Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal ControlCode0
Heuristics, Answer Set Programming and Markov Decision Process for Solving a Set of Spatial PuzzlesCode0
QFlip: An Adaptive Reinforcement Learning Strategy for the FlipIt Security GameCode0
Decision Making in Non-Stationary Environments with Policy-Augmented SearchCode0
Action Candidate Based Clipped Double Q-learning for Discrete and Continuous Action TasksCode0
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill DiscoveryCode0
Combining No-regret and Q-learningCode0
Playing Doom with SLAM-Augmented Deep Reinforcement LearningCode0
Hierarchical Reinforcement Learning with the MAXQ Value Function DecompositionCode0
Playing FPS Games with Deep Reinforcement LearningCode0
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