Identifying and exploiting weak-information inducing actions in solving POMDPs

We present a method for identifying actions that lead to observations which are only weakly informative in the context of partially observable Markov decision processes (POMDP). We call such actions as weak(inclusive of zero-) information inducing. Policy subtrees rooted at these actions may be computed more efficiently. While zero-information inducing actions may be exploited without error, the quicker backup for weak but non-zero information inducing actions may introduce error. We empirically demonstrate the substantial computational savings that exploiting such actions may bring to exact and approximate solutions of POMDPs while maintaining the solution quality.