finished adding tree_in_info arg and action_info

Project.toml

@@ -8,6 +8,7 @@ BasicPOMCP = "d721219e-3fc6-5570-a8ef-e5402f47c49e"
 BeliefUpdaters = "8bb6e9a1-7d73-552c-a44a-e5dc5634aac4"
 CPUTime = "a9c8d775-2e2e-55fc-8582-045d282d599e"
 D3Trees = "e3df1716-f71e-5df9-9e2d-98e193103c45"
+POMDPModelTools = "08074719-1b2a-587c-a292-00f91cc44415"
 POMDPs = "a93abf59-7444-517b-a68a-c42f96afdd7d"
 Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
 ParticleFilters = "c8b314e2-9260-5cf8-ae76-3be7461ca6d0"

README.md

@@ -87,16 +87,16 @@ Bounds need not be calculated independently; a single object or function that re
 [D3Trees.jl](https://github.com/sisl/D3Trees.jl) can be used to visualize the search tree, for example
 
 ```julia
-using POMDPs, POMDPModels, D3Trees, ARDESPOT
+using POMDPs, POMDPModels, POMDPModelTools, D3Trees, ARDESPOT
 
 pomdp = TigerPOMDP()
 
-solver = DESPOTSolver(bounds=(-20.0, 0.0))
+solver = DESPOTSolver(bounds=(-20.0, 0.0), tree_in_info=true)
 planner = solve(solver, pomdp)
 b0 = initialstate_distribution(pomdp)
 
-tree = ARDESPOT.build_despot(planner, b0)
-inchrome(D3Tree(tree, init_expand=5))
+a, info = action_info(planner, b0)
+inchrome(D3Tree(info[:tree], init_expand=5))
 ```
 will create an interactive tree that looks like this:
 

src/ARDESPOT.jl

@@ -8,6 +8,7 @@ using ParticleFilters
 using D3Trees
 using Random
 using Printf
+using POMDPModelTools
 
 using BasicPOMCP # for ExceptionRethrow and NoDecision
 import BasicPOMCP.default_action

src/pomdps_glue.jl

@@ -1,6 +1,6 @@
 POMDPs.solve(sol::DESPOTSolver, p::POMDP) = DESPOTPlanner(sol, p)
 
-function POMDPs.action_info(p::DESPOTPlanner, b)
+function POMDPModelTools.action_info(p::DESPOTPlanner, b)
     info = Dict{Symbol, Any}()
     try
         Random.seed!(p.rs, rand(p.rng, UInt32))

test/runtests.jl

@@ -5,6 +5,7 @@ using POMDPs
 using POMDPModels
 using POMDPSimulators
 using Random
+using POMDPModelTools
 
 include("memorizing_rng.jl")
 
@@ -71,7 +72,8 @@ rng = MersenneTwister(4)
 solver = DESPOTSolver(epsilon_0=0.1,
                       bounds=bounds,
                       rng=rng,
-                      random_source=MemorizingSource(500, 90, rng)
+                      random_source=MemorizingSource(500, 90, rng),
+                      tree_in_info=true
                      )
 p = solve(solver, pomdp)
 a = action(p, initialstate_distribution(pomdp))
@@ -80,6 +82,8 @@ include("random_2.jl")
 
 # visualization
 show(stdout, MIME("text/plain"), D)
+a, info = action_info(p, initialstate_distribution(pomdp))
+show(stdout, MIME("text/plain"), info[:tree])
 
 # from README:
 using POMDPs, POMDPModels, POMDPSimulators, ARDESPOT