BSDESPOT.jl

An implementation of the BSDESPOT (Better Selection DESPOT) online POMDP Solver. BSDESPOT is a variation of DESPOT. It provides action branch selection based on upper and lower bounds, and multi-observation branches selection.
Author LAMDA-POMDP
Popularity
1 Star
Updated Last
6 Months Ago
Started In
September 2020

BSDESPOT

An implementation of the BSDESPOT (Better Selection DESPOT) online POMDP Solver. BSDESPOT is a variation of DESPOT. It provides action branch selection based on upper and lower bounds, and multi-observation branches selection.

Installation

Pkg> registry add git@github.com:JuliaPOMDP/Registry.git
Pkg> add https://github.com/LAMDA-POMDP/BSDESPOT.jl # If a mature version is needed
Pkg> dev PATH/TO/BSDESPOT # If a version in development is needed, please first clone the project to the local.

Usage

using POMDPs, POMDPModels, POMDPSimulators, BSDESPOT

pomdp = TigerPOMDP()

solver = BS_DESPOTSolver(bounds=IndependentBounds(-20.0, 0.0))
planner = solve(solver, pomdp)

for (s, a, o) in stepthrough(pomdp, planner, "s,a,o", max_steps=10)
    println("State was $s,")
    println("action $a was taken,")
    println("and observation $o was received.\n")
end

Solver Options

For some detailed parameters of DESPOT, please refer to original ARDESPOT: https://github.com/JuliaPOMDP/ARDESPOT.jl.

Action Branch Selection

BSDESPOT provides two methods for selecting action branch based on upper and lower bounds: value-based, ranking-based. The default is ranking-based method. Usage is as follows:

solver = BS_DESPOTSolver(..., impl=:rank, ...) # Ranking-based
solver = BS_DESPOTSolver(..., impl=:val, ...) # Value-based

$\beta$ is the coefficient for adjusting the engagement of the lower bound. The default is 0 (only use upper bound selection).

solver = BS_DESPOTSolver(..., beta=0.1, ...) # How to adjust beta

Observation Branch Selection

$\zeta$ is the parameter to determine how close the branches are to the optimal ones will be selected. The default is 1 (only expand single observation branch). If you need to dynamically adjust $\zeta$ during planning, please define a function related to d and k (d is the ratio of current depth to the maximum depth, and k means the ratio of remaining scenarios in current belief to $K$), i.e.

# Define a function to adjust zeta dynamically. d is the ratio of the current depth to the maximum depth, k is the ratio of the number of current scenarios to K.
function f_zeta(d, k)
  1 - 0.1*k - 0.1*(1-d)
end
# When initializing the solver, specify the function
solver = BS_DESPOTSolver(..., adjust_zeta=f_zeta, ...)