update for CxxWrap 0.14 compat

Project.toml

@@ -1,14 +1,14 @@
 name = "OpenSpiel"
 uuid = "ceb70bd2-fe3f-44f0-b81f-41608acaf2f2"
 authors = ["Jun Tian <[email protected]>"]
-version = "0.1.5"
+version = "0.2.0"
 
 [deps]
 CxxWrap = "1f15a43c-97ca-5a2a-ae31-89f07a497df4"
 OpenSpiel_jll = "bd10a763-4654-5023-a028-c4918c6cd33e"
 
 [compat]
-CxxWrap = "0.12, 0.13, 0.14"
+CxxWrap = "0.14"
 OpenSpiel_jll = "1"
 julia = "1.6"
 

src/OpenSpiel.jl

@@ -5,7 +5,7 @@ using OpenSpiel_jll
 
 import CxxWrap:argument_overloads
 
-@wrapmodule(libspieljl)
+@wrapmodule(OpenSpiel_jll.get_libspieljl_path)
 
 include("patch.jl")
 

src/patch.jl

@@ -1,19 +1,19 @@
-Base.show(io::IO, g::CxxWrap.StdLib.SharedPtrAllocated{Game}) = print(io, to_string(g))
-Base.show(io::IO, s::CxxWrap.StdLib.UniquePtrAllocated{State}) = print(io, to_string(s))
+Base.show(io::IO, g::CxxWrap.StdLib.SharedPtrAllocated{Game}) = print(io, to_string(g[]))
+Base.show(io::IO, s::CxxWrap.StdLib.UniquePtrAllocated{State}) = print(io, to_string(s[]))
 Base.show(io::IO, gp::Union{GameParameterAllocated, GameParameterDereferenced}) = print(io, to_repr_string(gp))
 
 function Base.hash(s::CxxWrap.CxxWrapCore.SmartPointer{T}, h::UInt) where {T<:Union{Game,State}}
-    hash(to_string(s), h)
+    hash(to_string(s[]), h)
 end
 
 function Base.:(==)(s::CxxWrap.CxxWrapCore.SmartPointer{T}, ss::CxxWrap.CxxWrapCore.SmartPointer{T}) where {T<:Union{Game, State}}
-    to_string(s) == to_string(ss)
+    to_string(s[]) == to_string(ss[])
 end
 
 GameParameter(x::Int) = GameParameter(Ref(Int32(x)))
 
 Base.copy(s::CxxWrap.StdLib.UniquePtrAllocated{State}) = deepcopy(s)
-Base.deepcopy(s::CxxWrap.StdLib.UniquePtrAllocated{State}) = clone(s)
+Base.deepcopy(s::CxxWrap.StdLib.UniquePtrAllocated{State}) = clone(s[])
 Base.reshape(s::CxxWrap.StdLib.StdVectorAllocated, dims::Int32...) = reshape(s, Int.(dims))
 
 if Sys.KERNEL == :Linux
@@ -75,3 +75,77 @@ function load_game_as_turn_based(s::Union{String, CxxWrap.StdLib.StdStringAlloca
         _load_game_as_turn_based(s, StdMap{StdString, GameParameter}(ps))
     end
 end
+
+is_chance_node(state::CxxWrap.StdLib.UniquePtrAllocated{State}) = is_chance_node(state[])
+
+new_initial_state(game::CxxWrap.StdLib.SharedPtrAllocated{Game}) = new_initial_state(game[])
+
+legal_actions(state::CxxWrap.StdLib.UniquePtrAllocated{State}) = legal_actions(state[])
+
+child(state::CxxWrap.StdLib.UniquePtrAllocated{State}, i::Int64) = child(state[], i)
+
+is_terminal(state::CxxWrap.StdLib.UniquePtrAllocated{State}) = is_terminal(state[])
+
+information_state_string(state::CxxWrap.StdLib.UniquePtrAllocated{State}) = information_state_string(state[])
+
+get_uniform_policy(game::CxxWrap.StdLib.SharedPtrAllocated{Game}) = get_uniform_policy(game[])
+
+record_batched_trajectories(game::CxxWrap.StdLib.SharedPtrAllocated{Game}, p::CxxWrap.StdLib.StdVectorAllocated{TabularPolicy}, m::StdMapAllocated{StdString, Int32}, i::Int64, b::Bool, i2::Int64, i3::Int64) = record_batched_trajectories(game[], p, m, i, b, i2, i3)
+
+expected_returns(state::CxxWrap.StdLib.UniquePtrAllocated{State}, policy::CxxWrap.StdLib.SharedPtrAllocated{Policy}, i::Int64) = expected_returns(state[], policy[], i)
+
+exploitability(game::CxxWrap.StdLib.SharedPtrAllocated{Game}, policy::CxxWrap.StdLib.SharedPtrAllocated{Policy}) = exploitability(game[], policy[])
+
+current_player(state::CxxWrap.StdLib.UniquePtrAllocated{State}) = current_player(state[])
+
+action_to_string(state::CxxWrap.StdLib.UniquePtrAllocated{State}, i1, i2) = action_to_string(state[], i1, i2)
+
+apply_action(state::CxxWrap.StdLib.UniquePtrAllocated{State}, i::AbstractVector{<:Number}) = apply_action(state[], i)
+
+apply_action(state::CxxWrap.StdLib.UniquePtrAllocated{State}, i::Number) = apply_action(state[], i)
+
+restart_at(b::MCTSBotAllocated, s::CxxWrap.StdLib.UniquePtrAllocated{State}) = restart_at(b, s[])
+
+best_child(root::CxxWrap.StdLib.UniquePtrAllocated{SearchNode}) = best_child(root[])
+
+get_outcome(root::CxxWrap.StdLib.UniquePtrAllocated{SearchNode}) = get_outcome(root[])
+
+get_player(p::CxxWrap.StdLib.UniquePtrAllocated{SearchNode}) = get_player(p[])
+
+get_children(root::CxxWrap.StdLib.UniquePtrAllocated{SearchNode}) = get_children(root[])
+
+is_simultaneous_node(state::CxxWrap.StdLib.UniquePtrAllocated{State}) = is_simultaneous_node(state[])
+
+step(bot, state::CxxWrap.StdLib.UniquePtrAllocated{State}) = step(bot, state[])
+
+chance_outcomes(state::CxxWrap.StdLib.UniquePtrAllocated{State}) = chance_outcomes(state[])
+
+returns(state::CxxWrap.StdLib.UniquePtrAllocated{State}) = returns(state[])
+
+min_utility(game::CxxWrap.StdLib.SharedPtrAllocated{Game}) = min_utility(game[])
+
+max_utility(game::CxxWrap.StdLib.SharedPtrAllocated{Game}) = max_utility(game[])
+
+serialize_game_and_state(game::CxxWrap.StdLib.SharedPtrAllocated{Game}, state::CxxWrap.StdLib.UniquePtrAllocated{State}) = serialize_game_and_state(game[], state[])
+
+is_mean_field_node(state::CxxWrap.StdLib.UniquePtrAllocated{State}) = is_mean_field_node(state[])
+
+legal_actions(state::CxxWrap.StdLib.UniquePtrAllocated{State}, i) = legal_actions(state[], i)
+
+history(state::CxxWrap.StdLib.UniquePtrAllocated{State}) = history(state[])
+
+is_player_node(state::CxxWrap.StdLib.UniquePtrAllocated{State}) = is_player_node(state[])
+
+num_players(game::CxxWrap.StdLib.SharedPtrAllocated{Game}) = num_players(game[])
+
+distribution_support(state::CxxWrap.StdLib.UniquePtrAllocated{State}) = distribution_support(state[])
+
+get_type(game::CxxWrap.StdLib.SharedPtrAllocated{Game}) = get_type(game[])
+
+update_distribution(state::CxxWrap.StdLib.UniquePtrAllocated{State}, dist::CxxWrap.StdLib.StdVectorAllocated{Float64}) = update_distribution(state[], dist)
+
+num_cols(game::CxxWrap.StdLib.SharedPtrAllocated{MatrixGame}) = num_cols(game[])
+
+num_rows(game::CxxWrap.StdLib.SharedPtrAllocated{MatrixGame}) = num_rows(game[])
+
+extensive_to_matrix_game(game::CxxWrap.StdLib.SharedPtrAllocated{Game}) = extensive_to_matrix_game(game[])

test/bots.jl

@@ -3,7 +3,7 @@
 @testset "MCTSBot" begin
     UCT_C = 2.
 
-    init_bot(game, max_simulations, evaluator) = MCTSBot(game, evaluator, UCT_C, max_simulations, 5, true, 42, false, UCT, 0., 0.)
+    init_bot(game, max_simulations, evaluator) = MCTSBot(game[], evaluator, UCT_C, max_simulations, 5, true, 42, false, UCT, 0., 0.)
 
     @testset "can play tic_tac_toe" begin
         game = load_game("tic_tac_toe")
@@ -51,8 +51,8 @@
             apply_action(state, get_action_by_str(state, action_str))
         end
         evaluator = random_rollout_evaluator_factory(20, 42)
-        bot = MCTSBot(game, evaluator, UCT_C, 10000, 10, true, 42, false, UCT, 0., 0.)
-        mcts_search(bot, state), state
+        bot = MCTSBot(game[], evaluator, UCT_C, 10000, 10, true, 42, false, UCT, 0., 0.)
+        mcts_search(bot, state[]), state[]
     end
 
     @testset "solve draw" begin

test/cfr.jl

@@ -15,7 +15,7 @@ test_exploitability_kuhn_poker(game, policy) = @test exploitability(game, policy
 
 @testset "CFRSolver" begin
     game = load_game("kuhn_poker")
-    solver = CFRSolver(game)
+    solver = CFRSolver(game[])
     for _ in 1:300
         evaluate_and_update_policy(solver)
     end
@@ -26,7 +26,7 @@ end
 
 @testset "CFRPlusSolver" begin
     game = load_game("kuhn_poker")
-    solver = CFRPlusSolver(game)
+    solver = CFRPlusSolver(game[])
     for _ in 1:200
         evaluate_and_update_policy(solver)
     end