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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 1901–1918 of 1918 papers

TitleStatusHype
Personalized Medical Treatments Using Novel Reinforcement Learning Algorithms—0
Single-Agent vs. Multi-Agent Techniques for Concurrent Reinforcement Learning of Negotiation Dialogue Policies—0
Empirically Evaluating Multiagent Learning Algorithms—0
Adaptive Stochastic Resource Control: A Machine Learning Approach—0
Optimal Demand Response Using Device Based Reinforcement Learning—0
Two Timescale Convergent Q-learning for Sleep--Scheduling in Wireless Sensor Networks—0
Q-learning optimization in a multi-agents system for image segmentation—0
Risk-sensitive Reinforcement Learning—0
Approximate Kalman Filter Q-Learning for Continuous State-Space MDPs—0
The association problem in wireless networks: a Policy Gradient Reinforcement Learning approach—0
Projective simulation for classical learning agents: a comprehensive investigation—0
Hybrid Q-Learning Applied to Ubiquitous recommender system—0
Speedy Q-Learning—0
Double Q-learning—0
Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation—0
Least-Squares Policy IterationCode0
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning—0
Hierarchical Reinforcement Learning with the MAXQ Value Function DecompositionCode0
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