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2000
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Eligibility Traces for Off-Policy Policy Evaluation

10 years 6 months ago
Eligibility Traces for Off-Policy Policy Evaluation
Eligibility traces have been shown to speed reinforcement learning, to make it more robust to hidden states, and to provide a link between Monte Carlo and temporal-difference methods. Here we generalize eligibility traces to off-policy learning, in which one learns about a policy different from the policy that generates the data. Off-policy methods can greatly multiply learning, as many policies can be learned about from the same data stream, and have been identified as particularly useful for learning about subgoals and temporally extended macro-actions. In this paper we consider the off-policy version of the policy evaluation problem, for which only one eligibility trace algorithm is known, a Monte Carlo method. We analyze and compare this and four new eligibility trace algorithms, emphasizing their relationships to the classical statistical technique known as importance sampling. Our main results are 1) to establish the consistency and bias properties of the new methods and 2) to e...
Doina Precup, Richard S. Sutton, Satinder P. Singh
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2000
Where ICML
Authors Doina Precup, Richard S. Sutton, Satinder P. Singh
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