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Abstract:
decision problems (POMDPs) remains one of the most
challenging areas of research in stochastic planning. One line of
research in this area involves the use of reinforcement learning
with belief states, probability distributions over the underlying
model states. This is a promising method for small problems, but
its application is limited by the intractability of computing or
representing a full belief state for large problems. Recent work
shows that, in many settings, we can maintain an {\em approximate
belief state}, which is fairly close to the true belief state. In
particular, great success has been shown with approximate belief
states that ignore correlations between state variables. In this
paper, we investigate two methods of full belief state
reinforcement learning and one novel method for reinforcement
learning using factored approximate belief states. We compare the
performance of these algorithms on several well-known problem from
the literature. Our results demonstrate the importance of
approximate belief state representations for large problems.
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