This is a fork of the Julia 0.6 version of
POMDPToolbox.jl that has been edited to be compatible with robust POMDPs in addition to standard POMDPs. It is primarily used for simulation and belief updating.
This application is built for Julia 0.6. If not already installed, the application can be cloned using
See POMDPToolbox.jl for details on usage.
The robust extension to the base fork primarily focuses on belief updates and simulation.
using RPOMDPToolbox using RPOMDPModels, RPOMDPs, RobustValueIteration rpomdp = RockRIPOMDP() b = [psample(zeros(4), ones(4)) for i = 1:10] solver = RPBVISolver(beliefpoints = b, max_iterations = 10) policy = RobustValueIteration.solve(solver, rpomdp) rng = MersenneTwister(0) bu = updater(policy) binit = initial_belief_distribution(rpomdp) sinit = rand(rng, initial_state_distribution(rpomdp)) sim = RolloutSimulator(max_steps = 100) simval, simpercent = simulate(sim, rpomdp, policy, bu, binit, sinit)
To solve robust POMDP models, see RobustValueIteration.
If this code is useful to you, please star this package and consider citing the following paper.
Egorov, M., Sunberg, Z. N., Balaban, E., Wheeler, T. A., Gupta, J. K., & Kochenderfer, M. J. (2017). POMDPs.jl: A framework for sequential decision making under uncertainty. Journal of Machine Learning Research, 18(26), 1–5.
9 months ago