An implementation of the AR-DESPOT (Anytime Regularized DEterminized Sparse Partially Observable Tree) online POMDP Solver.
Tried to match the pseudocode from this paper: http://bigbird.comp.nus.edu.sg/m2ap/wordpress/wp-content/uploads/2017/08/jair14.pdf as closely as possible. Look there for definitions of all symbols.
Problems use the POMDPs.jl generative interface.
If you are trying to use this package and require more documentation, please file an issue!
On Julia v0.7 or later, ARDESPOT is in the JuliaPOMDP registry
Pkg.add("POMDPs") using POMDPs POMDPs.add_registry() Pkg.add("ARDESPOT")
using POMDPs, POMDPModels, POMDPSimulators, ARDESPOT pomdp = TigerPOMDP() solver = DESPOTSolver(bounds=(-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 can be found in the
julia> ?DESPOTSolver ... epsilon_0 :: Float64 xi :: Float64 K :: Int64 D :: Int64 lambda :: Float64 T_max :: Float64 max_trials :: Int64 bounds :: Any default_action :: Any rng :: AbstractRNG random_source :: ARDESPOT.DESPOTRandomSource bounds_warnings :: Bool
Each can be set with a keyword argument in the DESPOTSolver constructor. The definitions of the parameters match as closely as possible to the corresponding definition in the pseudocode of this paper.
Bounds can be specified as a tuple
(lower_bound, upper_bound), where the lower and upper bounds are one of
f- in this case
f(pomdp, b::ScenarioBelief)is called to determine the bound.
bound- in this case
ARDESPOT.lbound(bound, pomdp, b::ScenarioBelief)or
ARDESPOT.ubound(...)will be called to determine the bound.
To access the state particles in a
collect(particles(b)) to get a vector).
A common lower bound strategy is to use a rollout policy. This is provided by the
DefaultPolicyLB type, for example
will use a random rollout policy to calculate a lower bound.
IndependentBounds object adds some robustness. For example
DESPOTSolver(bounds=IndependentBounds(DefaultPolicyLB(RandomSolver()), 0.0, check_terminal=true, consistency_fix_thresh=0.1))
will automatically correct some common problems arising from using approximate bounds.
Bounds need not be calculated independently; a single object or function that returns a tuple can be passed to the
bounds argument. More examples can be found in src/bounds.jl. Please file an issue if more documentation is needed.
D3Trees.jl can be used to visualize the search tree, for example
using POMDPs, POMDPModels, POMDPModelTools, D3Trees, ARDESPOT pomdp = TigerPOMDP() solver = DESPOTSolver(bounds=(-20.0, 0.0), tree_in_info=true) planner = solve(solver, pomdp) b0 = initialstate_distribution(pomdp) a, info = action_info(planner, b0) inchrome(D3Tree(info[:tree], init_expand=5))
will create an interactive tree that looks like this:
DESPOT.jl was designed to exactly emulate the C++ code released by the original DESPOT developers. This implementation was designed to be as close to the pseudocode from the journal paper as possible for the sake of readability. ARDESPOT has a few more features (for example DESPOT.jl does not implement regularization and pruning), and has more compatibility with a wider range of POMDPs.jl problems because it does not emulate the C++ code.
8 months ago