Feb 28, 2016 · This paper contains two main contributions. First, we derive two new hybrid TD policy-evaluation algorithms, which fill a gap in this collection ...
ABSTRACT. Off-policy reinforcement learning has many applications in- cluding: learning from demonstration, learning multiple goal.
[PDF] Investigating Practical Linear Temporal Difference Learning ...
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This paper derives two new hybrid TD policy-evaluation algorithms, which fill a gap in this collection of algorithms and performs an empirical comparison to ...
This paper contains two main contributions. First, we derive two new hybrid TD policy-evaluation algorithms, which fill a gap in this collection of algorithms.
First, we derive two new hybrid TD policy-evaluation algorithms, which fill a gap in this collection of algorithms. Second, we perform an empirical comparison ...
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TD learning is an unsupervised technique in which the learning agent learns to predict the expected value of a variable occurring at the end of a sequence of ...
The evaluation function learned in Samuel's study was a linear function of the input variables, whereas multilayer networks learn more complex nonlinear.
Bibliographic details on Investigating practical, linear temporal difference learning.
This paper examines whether temporal difference methods for training connectionist networks, such as Suttons's TO('\) algorithm, can be suc-.
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They allow a system to learn to predict the total amount of reward expected over time, and they can be used for other prediction problems as well (Anderson, ...