Technical Note: \cal Q-Learning
Machine Learning
Reinforcement learning for the adaptive control of perception and action
Reinforcement learning for the adaptive control of perception and action
Reinforcement learning for robots using neural networks
Reinforcement learning for robots using neural networks
Reinforcement learning with replacing eligibility traces
Machine Learning - Special issue on reinforcement learning
Machine Learning - Special issue on reinforcement learning
Incremental multi-step Q-learning
Machine Learning - Special issue on reinforcement learning
Artificial Intelligence Review - Special issue on lazy learning
Neuro-Dynamic Programming
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Efficient Exploration In Reinforcement Learning
Efficient Exploration In Reinforcement Learning
Journal of Artificial Intelligence Research
Reinforcement Learning Soccer Teams with Incomplete World Models
Autonomous Robots
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
Fast concurrent reinforcement learners
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
From Q(λ) to average Q-learning: efficient implementation of an asymptotic approximation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Learning efficient policies for vision-based navigation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Efficient vision-based navigation
Autonomous Robots
Exploiting Best-Match Equations for Efficient Reinforcement Learning
The Journal of Machine Learning Research
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Q(λ)-learning uses TD(λ)-methods toaccelerate Q-learning. The update complexity of previous onlineQ(λ) implementations based on lookup tables is bounded by thesize of the state/action space. Our faster algorithm‘s updatecomplexity is bounded by the number of actions. The method is basedon the observation that Q-value updates may be postponed until theyare needed.