| Parameter | Type | Default | Description |
|---|---|---|---|
max_depth |
Int |
20 |
Maximum tree search depth |
n_particles |
Int |
100 |
Number of particles representing tree node belief |
criterion |
Any |
PolyUCB(1.) |
Action selection criterion |
k_o |
Float64 |
10.0 |
Initial observation widening parameter |
alpha_o |
Float64 |
0.0 |
Observation progressive widening parameter |
k_a |
Float64 |
5.0 |
Initial action widening parameter |
alpha_a |
Float64 |
0.0 |
Action progressive widening parameter |
tree_queries |
Int |
1_000 |
Maximum number of tree search iterations |
max_time |
Float64 |
Inf |
Maximum tree search time (in seconds) |
rng |
AbstractRNG |
Random.default_rng() |
Random number generator |
value_estimator |
Any |
FastRandomSolver() |
Belief node value estimator |
check_repeat_obs |
Bool |
true |
Check that repeat observations do not overwrite beliefs (added dictionary overhead) |
enable_action_pw |
Bool |
false |
Incrementally sample and add actions if true; add all actions at once if false |
beliefcache_size |
Int |
1_000 |
Number of particle/weight vectors to cache offline (reduces online array allocations) |
treecache_size |
Int |
1_000 |
Number of belief/action nodes to preallocate in tree (reduces Base._growend! calls) |
using POMDPs, POMDPModels
using ParticleFilterTrees
pomdp = LightDark1D()
b0 = initialstate(pomdp)
solver = PFTDPWSolver(tree_queries=10_000, check_repeat_obs=false)
planner = solve(solver, pomdp)
a = action(planner, b0)Using sufficiently large treecache_size and beliefcache_size allows for very few online allocations.
using BenchmarkTools
pomdp = LightDark1D()
b0 = initialstate(pomdp)
solver = SparsePFTSolver(
max_time = 0.1,
tree_queries = 100_000,
treecache_size = 50_000,
beliefcache_size = 50_000,
check_repeat_obs = false
)
planner = solve(solver, pomdp)
action(planner, b0)
@btime action(planner, b0)100.006 ms (0 allocations: 0 bytes)