Jan 16, 2013 · Our method applies to arbitrary POMDPs, including ones with infinite state and action spaces. We also present empirical results for our approach ...

Our approach is based on the following observation: Any (PO)MDP can be transformed into an “equivalent” POMDP in which all state transitions (given the current ...

We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observ.

This work proposes a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov ...

PDF | We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov.

Jun 30, 2000 · We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable ...

Jun 30, 2000 · PEGASUS: A policy search method for large MDPs and POMDPs.

PEGASUS: A policy search method for large MDPs and POMDPs. We propose a new approach to the problem of searching a space of policies for a Markov decision ...

Ng, A.Y., Jordan, M.: PEGASUS: A policy search method for large MDPs and POMDPs. In: Proc. of Uncertainty in Artificial Intelligence (2000). Google Scholar.

PEGASUS: A policy search method for large MDPs and. POMDPs, 2000 - Andrew Ng and Michael Jordan. • Shaping and Policy Search in Reinforcement Learning,.