We investigate a model for planning under uncertainty with temporally extended actions, where multiple actions can be taken concurrently at each decision epoch. Our model is based on the options framework, and combines it with factored state space models, where the set of options can be partitioned into classes that affect disjoint state variables.
We show that the set of decision epochs for concurrent options defines a semi-Markov decision process, if the underlying temporally extended actions being parallelized are restricted to Markov options.
This property allows us to use SMDPalgorithms for computing the value function over concurrent options. The concurrent options model allows overlapping execution of options in order to achieve higher performance or in order to perform a complex task.
We describe a simple experiment using a navigation task which illustrates how concurrent options results in a faster plan when compared to the case when only one option is taken at a time.