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ATAL
2008
Springer

Sigma point policy iteration

8 years 4 months ago
Sigma point policy iteration
In reinforcement learning, least-squares temporal difference methods (e.g., LSTD and LSPI) are effective, data-efficient techniques for policy evaluation and control with linear value function approximation. These algorithms rely on policy-dependent expectations of the transition and reward functions, which require all experience to be remembered and iterated over for each new policy evaluated. We propose to summarize experience with a compact policy-independent Gaussian model. We show how this policyindependent model can be transformed into a policy-dependent form and used to perform policy evaluation. Because closed-form transformations are rarely available, we introduce an efficient sigma point approximation. We show that the resulting Sigma-Point Policy Iteration algorithm (SPPI) is mathematically equivalent to LSPI for tabular representations and empirically demonstrate comparable performance for approximate representations. However, the experience does not need to be saved or re...
Michael H. Bowling, Alborz Geramifard, David Winga
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where ATAL
Authors Michael H. Bowling, Alborz Geramifard, David Wingate
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