Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds described by partially observable Markov Decision Processes (POMDPs), where an ...
We model reinforcement learning as the problem of learning to control a Partially Observable Markov Decision Process ( ¢¡¤£¦¥§ ), and focus on gradient ascent approache...
The software suite we will demonstrate at AAAI '06 was designed around planning with factored Markov decision processes (MDPs). It is a user-friendly suite that facilitates d...
Krol Kevin Mathias, Casey Lengacher, Derek William...
This paper presents an adaptation of the standard quantum search technique to enable application within Dynamic Programming, in order to optimise a Markov Decision Process. This i...
We develop a hierarchical approach to planning for partially observable Markov decision processes (POMDPs) in which a policy is represented as a hierarchical finite-state control...