Searching for "Learning Rates for Q-learning." – sorted by Relevance.
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Learning rates for Q-Learning
- Learning rates for Q-Learning Eyal Even-Dar Yishay Mansour y February 26, 2001 Abstract
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The Effect of Representation and Knowledge on Goal-Directed Exploration with Reinforcement-Learning
- ) + ff(r(s; a; s 0 ) + flU (s 0 ))," where ff 2 (0; 1] is called the learning rate. This Q-learning
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Reinforcement Learning in Continuous Time: Advantage Updating
- . This is equivalent to W new ���� (1 - a )W old + aK (6) where the learning rate a is a small positive number. Q-LEARNING
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Consideration of Risk in Reinforcement Learning
- all Q-values are initialized as in $ Q - Learning and if the learning rates in Q-Learning
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Convergence and divergence in standard and averaging reinforcement learning
- (st,at)+α(rt + γ max a Q(st+1,a) − Q(st,at)) Where 0 <α≤ 1 is the learning rate. Q-learning is an off
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Experiments in Robot Control for an Instance-Based Reinforcement Learning Algorithm based on Prior
- requirements. For instance, the learning rate for Q-learning must follow a 1=t intensity time variation
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Utile coordination: Learning interdependencies among cooperative agents
- (s, a)+α[R(s, a)+γ max a ′ Q(s′ , a ′ )−Q(s, a)] (2) where α ∈ (0, 1) is an appropriate learning rate. Q-learning
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Privacy-Preserving Reinforcement Learning
- (st,at) ← ∆Q(st,at)+Q(st,at), (1) where α is the learning rate. Q-learning is obtained by replacing the update
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