A Planning Algorithm for Predictive State Representations
Abstract
We address the problem of optimally controlling stochastic environments that are partially observable. The standard method for tackling such problems is to define and solve a Partially Observable Markov Decision Process (POMDP). However, it is well known that exactly solving POMDPs is very costly computationally. Recently, Littman, Sutton and Singh (2002) have proposed an alternative representation of partially observable environments, called predictive state representations (PSRs). PSRs are grounded in the sequence of actions and observations of the agent, and hence relate the state representation directly to the agent’s experience. In this paper, we present a policy iteration
Cite
Text
Izadi and Precup. "A Planning Algorithm for Predictive State Representations." International Joint Conference on Artificial Intelligence, 2003.Markdown
[Izadi and Precup. "A Planning Algorithm for Predictive State Representations." International Joint Conference on Artificial Intelligence, 2003.](https://mlanthology.org/ijcai/2003/izadi2003ijcai-planning/)BibTeX
@inproceedings{izadi2003ijcai-planning,
title = {{A Planning Algorithm for Predictive State Representations}},
author = {Izadi, Masoumeh T. and Precup, Doina},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2003},
pages = {1520-1521},
url = {https://mlanthology.org/ijcai/2003/izadi2003ijcai-planning/}
}