pomdp

Partially observable Markov Decision Processes

A problem formulation that enable optimal sequential decisions to be made in uncertain environments (Intro to POMDPs.jl course)

Abstractions are general to [[decision-theory]]: agent makes decisions based on current state $s$, choosing action $a$ that is motivated to maximize the expected reward $r$ according to a policy $\pi$

Types of MDP solvers

• Discrete value iteration
• Local approximation value iteration
• Global approximation value iteration
• Monte Carlo tree search

Notes on the Julia implementation, POMDP.jl