Implementing Markov Processes with Julia

Approved: Yes
Title: Implementing Markov Processes with Julia
Category: Tool
Skill Level:
Intermediate (305), Advanced (306)

Description:
The following link provides an easy method of implementing Markov Decision
Processes (MDP) in the Julia computing language. MDPs are a class of
algorithms designed to handle stochastic situations where the actor has some
level of control. For example, used at a low level, MDPs can be used to
control an inverted pendulum, but applied in higher level decision making the
can also decide when to take evasive action in air traffic management. MDPs
can also be extended to the partially observable domain to form the Partially
Observable Markov Decision Process (POMDP). This link contains a wealth of
information to show one can easily implement basic POMDP and MDP algorithms
and apply well known online and offline solvers.


Link to Resource:
- Markov Decision Processes in Julia (https://juliapomdp.github.io/POMDPs.jl/latest/)

Tags:
ai (271), machine-learning (272), julia (72)

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