Learning MDP action models via discrete mixture trees Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/hh63t030f

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  • This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the conditional probability distributions of the DBNs. Existing algorithms presume that the stochasticity in the domain can be modeled as a deterministic function with additive noise. This is inappropriate for many RL domains, where the stochasticity takes the form of a random choice over deterministic functions. This paper introduces a regression tree algorithm in which each leaf node is modeled as a finite mixture of deterministic functions. This mixture is approximated via a greedy set cover. To combat overfitting, pruning techniques incorporating log likelihood and KL-Divergence are employed. Experiments on three challenging RL domains, two with stochastic variants, show that this approach finds trees that are more accurate and that are more likely to correctly identify the conditional dependencies in the DBNs based on small samples.
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