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- ...
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Oct 8, 2015 · If the learning rate is constant, will Q function converge to the optimal on or learning rate should necessarily decay to guarantee convergence?
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 the ...
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 ...
May 12, 2019 · For instance, it is possible to apply tabular Q-learning to Tic Tac Toe with a learning rate of 1.0 - essentially replacing each estimate with ...
Q-Learning Explained: Learn Reinforcement Learning Basics
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Sep 17, 2024 · Learning Rate (α): A factor determining how much new information overrides old information. A higher learning rate means the agent learns faster ...
Apr 17, 2021 · The learning rate represents how much weight you want to assign to the last update vs the previous values. If you use alpha = 1, you are saying ...
Jan 17, 2020 · The sparser and farther away rewards are, the higher you probably want this. Learning rate: between 0.1 and 0.00001. Layers: At most 3 in most ...
Q-learning is a model-free reinforcement learning algorithm that teaches an agent to assign values to each action it might take, conditioned on the agent ...
Apr 24, 2020 · The learning rate should be in the range of 0 -1. The higher the learning rate, it quickly replaces the new q value. We need to optimize it ...