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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 441–450 of 1918 papers

TitleStatusHype
Data-driven inventory management for new products: An adjusted Dyna-Q approach with transfer learning—0
Data-Driven Knowledge Transfer in Batch Q^* Learning—0
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation—0
Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control—0
Data-Efficient Quadratic Q-Learning Using LMIs—0
DDPG based on multi-scale strokes for financial time series trading strategy—0
Breaking the Deadly Triad with a Target Network—0
DECAF: Learning to be Fair in Multi-agent Resource Allocation—0
Decentralised Q-Learning for Multi-Agent Markov Decision Processes with a Satisfiability Criterion—0
An Attempt to Model Human Trust with Reinforcement Learning—0
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