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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 171–180 of 1918 papers

TitleStatusHype
Dual Ensembled Multiagent Q-Learning with Hypernet RegularizerCode0
Enhancing Robot Assistive Behaviour with Reinforcement Learning and Theory of MindCode0
From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no LibrariesCode0
AFU: Actor-Free critic Updates in off-policy RL for continuous controlCode0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
A Framework for Automated Cellular Network Tuning with Reinforcement LearningCode0
Distributionally Robust Deep Q-LearningCode0
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy ImprovementCode0
Diagnosing Bottlenecks in Deep Q-learning AlgorithmsCode0
Double Q-PID algorithm for mobile robot controlCode0
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