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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
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis—0
An Attempt to Model Human Trust with Reinforcement Learning—0
Robust and Data-efficient Q-learning by Composite Value-estimation—0
^2-exploration for Reinforcement Learning—0
Bootstrapped Hindsight Experience replay with Counterintuitive Prioritization—0
Adaptive Q-learning for Interaction-Limited Reinforcement Learning—0
Offline Reinforcement Learning with In-sample Q-LearningCode1
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration—0
Towards Unknown-aware Deep Q-Learning—0
Q-learning for real time control of heterogeneous microagent collectives—0
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