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In this paper we derive convergence rates for Q-learning. We show an interesting relationship between the convergence rate and the learning rate used in Q-learning.

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186 J.N. TSITSIKLIS The convergence of Q-learning has been proved by Watkins and Dayan (1992) for dis-counted Markov decision problems, as well as for undiscounted

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Algorithm Trading using Q-Learning and Recurrent

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On Boundedness of Q-Learning Iterates for Stochastic

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Nash Q-Learning for General-Sum Stochastic Games Q-learning, the performance of both agents is better than using single-agent Q-learning. We have

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Yu and Bertsekas: Boundedness of Q-Learning for Stochastic Shortest Path Problems Mathematics of Operations Research 38(2), pp. 209–227, ©2013 INFORMS 211

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Q-Learning Basics At each step s, choose the action a which maximizes the function Q(s, a) – Q is the estimated utility function – it tells us how

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Learning Rates for Q-learning - Princeton University

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modeling stock trading with multiple Q-learning agents

Learning Rates for Q-learning 2. The Model We define a Markov Decision process (MDP) as follows Definition 1 A Markov Decision process (MDP) M is a 4-tuple (S,U,P,R

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Q learning in a stochastic environment - Cross Validated

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Algorithm Trading using Q-Learning and Recurrent Reinforcement Learning Qxa p aDxya r Q yb=+ In Q-learning,

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Learning Rates for Q-learning

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