  The partially observable Markov decision process (POMDP) provides a
principled general framework for planning under uncertainty, but solving POMDPs
optimally is computationally intractable, due to the "curse of dimensionality"
and the "curse of history". To overcome these challenges, we introduce the
Determinized Sparse Partially Observable Tree (DESPOT), a sparse approximation
of the standard belief tree, for online planning under uncertainty. A DESPOT
focuses online planning on a set of randomly sampled scenarios and compactly
captures the "execution" of all policies under these scenarios. We show that
the best policy obtained from a DESPOT is near-optimal, with a regret bound
that depends on the representation size of the optimal policy. Leveraging this
result, we give an anytime online planning algorithm, which searches a DESPOT
for a policy that optimizes a regularized objective function. Regularization
balances the estimated value of a policy under the sampled scenarios and the
policy size, thus avoiding overfitting. The algorithm demonstrates strong
experimental results, compared with some of the best online POMDP algorithms
available. It has also been incorporated into an autonomous driving system for
real-time vehicle control. The source code for the algorithm is available
online.
