upgraded to POMDPTools

Project.toml

@@ -1,15 +1,13 @@
 name = "MCTS"
 uuid = "e12ccd36-dcad-5f33-8774-9175229e7b33"
 repo = "https://github.com/JuliaPOMDP/MCTS.jl"
-version = "0.5.0"
+version = "0.5.1"
 
 [deps]
 Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
 D3Trees = "e3df1716-f71e-5df9-9e2d-98e193103c45"
 POMDPLinter = "f3bd98c0-eb40-45e2-9eb1-f2763262d755"
-POMDPModelTools = "08074719-1b2a-587c-a292-00f91cc44415"
-POMDPPolicies = "182e52fb-cfd0-5e46-8c26-fd0667c990f4"
-POMDPSimulators = "e0d0a172-29c6-5d4e-96d0-f262df5d01fd"
+POMDPTools = "7588e00f-9cae-40de-98dc-e0c70c48cdd7"
 POMDPs = "a93abf59-7444-517b-a68a-c42f96afdd7d"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
@@ -19,9 +17,7 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Colors = "0.9, 0.10, 0.11, 0.12"
 D3Trees = "0.3"
 POMDPLinter = "0.1"
-POMDPModelTools = "0.3"
-POMDPPolicies = "0.3, 0.4"
-POMDPSimulators = "0.3"
+POMDPTools = "0.1"
 POMDPs = "0.9"
 ProgressMeter = "1"
 julia = "1"

bench/non-terminal_gw.jl

@@ -1,7 +1,7 @@
 using POMDPs
 using POMDPModels
 using MCTS
-using POMDPSimulators
+using POMDPTools
 using ProgressMeter
 using Random
 # using ProfileView

bench/non-terminal_gw_dpw.jl

@@ -1,7 +1,7 @@
 using POMDPs
 using POMDPModels
 using MCTS
-using POMDPSimulators
+using POMDPTools
 using ProgressMeter
 using Random
 # using ProfileView

docs/src/index.md

@@ -17,7 +17,7 @@ POMDPs.add("MCTS")
 
 ## Usage
 
-Problems should be defined using the [POMDPs.jl generative interface](https://juliapomdp.github.io/POMDPs.jl/stable/generative/).
+Problems should be defined using the [POMDPs.jl generative interface](https://juliapomdp.github.io/POMDPs.jl/stable/def_pomdp/#Using-a-single-generative-function-instead-of-separate-T,-Z,-and-R).
 
 To see the methods that you need to implement to use MCTS with your MDP (assume you're defining an MDP of type `MyMDP` with states represented by integers and 3 possible integer actions), run
 ```julia
@@ -49,9 +49,9 @@ For rollout(::SolvedRolloutEstimator, ::MDP, ::Any, ::Int64) (in estimate_value(
 For simulate(::RolloutSimulator, ::MDP, ::Policy, ::Any) (in rollout(::SolvedRolloutEstimator, ::MDP, ::Any, ::Int64)):
   [✔] action(::RandomPolicy, ::Int64)
 ```
-indicating that the generative interface still needs to be implemented for `MyMDP` to be used with MCTS. See the [geneartive interface documentation](http://juliapomdp.github.io/POMDPs.jl/stable/generative/) for further details.
+indicating that the generative interface still needs to be implemented for `MyMDP` to be used with MCTS. See the [geneartive interface documentation](https://juliapomdp.github.io/POMDPs.jl/stable/def_pomdp/#Using-a-single-generative-function-instead-of-separate-T,-Z,-and-R) for further details.
 
-Note: MDPs that implement the [POMDPs.jl explicit interface](http://juliapomdp.github.io/POMDPs.jl/stable/explicit/) can also be used with MCTS since the implementation of the explicit interface automatically defines the functions in the generative interface.
+Note: MDPs that implement the [POMDPs.jl explicit interface](https://juliapomdp.github.io/POMDPs.jl/stable/def_pomdp/) can also be used with MCTS since the implementation of the explicit interface automatically defines the functions in the generative interface.
 
 Once the above functions are defined, the solver can be called with the following syntax:
 

src/MCTS.jl

@@ -1,9 +1,7 @@
 module MCTS
 
 using POMDPs
-using POMDPModelTools
-using POMDPPolicies
-using POMDPSimulators
+using POMDPTools
 using Random
 using Printf
 using ProgressMeter

src/dpw.jl

@@ -15,7 +15,7 @@ POMDPs.action(p::DPWPlanner, s) = first(action_info(p, s))
 """
 Construct an MCTSDPW tree and choose the best action. Also output some information.
 """
-function POMDPModelTools.action_info(p::DPWPlanner, s; tree_in_info=false)
+function POMDPTools.action_info(p::DPWPlanner, s; tree_in_info=false)
     local a::actiontype(p.mdp)
     info = Dict{Symbol, Any}()
     try

src/vanilla.jl

@@ -199,7 +199,7 @@ POMDPs.solve(solver::MCTSSolver, mdp::Union{POMDP,MDP}) = MCTSPlanner(solver, md
     @subreq simulate(policy, state, policy.solver.depth)
 end
 
-function POMDPModelTools.action_info(p::AbstractMCTSPlanner, s)
+function POMDPTools.action_info(p::AbstractMCTSPlanner, s)
     tree = plan!(p, s)
     best = best_sanode_Q(StateNode(tree, s))
     return action(best), (tree=tree,)

test/benchmarks.jl

@@ -1,7 +1,7 @@
 using POMDPs
 using MCTS
 using POMDPModels
-using POMDPSimulators
+using POMDPTools
 
 # function POMDPs.simulate(mdp::POMDP,
 #                          policy::Policy,

test/runtests.jl

@@ -5,7 +5,7 @@ using Test
 using NBInclude
 using D3Trees
 using Random
-using POMDPPolicies
+using POMDPTools
 using POMDPLinter: @requirements_info
 
 n_iter = 50