Int64 -> Int

Author: jbrea

src/environments/atari.jl

@@ -9,7 +9,7 @@ struct AtariEnv{To,F} <: AbstractEnv
     actions::Array{Int32, 1}
     action_space::DiscreteSpace{Int}
     observation_space::To
-    noopmax::Int64
+    noopmax::Int
 end
 
 action_space(env::AtariEnv) = env.action_space

src/environments/classic_control/cart_pole.jl

@@ -14,7 +14,7 @@ struct CartPoleEnvParams{T}
     tau::T
     thetathreshold::T
     xthreshold::T
-    max_steps::Int64
+    max_steps::Int
 end
 
 mutable struct CartPoleEnv{T, R<:AbstractRNG} <: AbstractEnv
@@ -24,7 +24,7 @@ mutable struct CartPoleEnv{T, R<:AbstractRNG} <: AbstractEnv
     state::Array{T, 1}
     action::Int
     done::Bool
-    t::Int64
+    t::Int
     rng::R
 end
 

src/environments/classic_control/mdp.jl

@@ -63,7 +63,7 @@ MDPEnv(model; rng=Random.GLOBAL_RNG) = MDPEnv(
 action_space(env::Union{MDPEnv, POMDPEnv}) = env.action_space
 observation_space(env::Union{MDPEnv, POMDPEnv}) = env.observation_space
 
-observationindex(env, o) = Int64(o) + 1
+observationindex(env, o) = Int(o) + 1
 
 function reset!(env::Union{POMDPEnv, MDPEnv})
     initialstate(env.model, env.rng)
@@ -89,13 +89,13 @@ end
 #####
 """
     mutable struct SimpleMDPEnv
-        ns::Int64
-        na::Int64
-        state::Int64
+        ns::Int
+        na::Int
+        state::Int
         trans_probs::Array{AbstractArray, 2}
         reward::R
-        initialstates::Array{Int64, 1}
-        isterminal::Array{Int64, 1}
+        initialstates::Array{Int, 1}
+        isterminal::Array{Int, 1}
         rng::S
 A Markov Decision Process with `ns` states, `na` actions, current `state`,
 `na`x`ns` - array of transition probabilites `trans_props` which consists for
@@ -110,11 +110,11 @@ probabilities) `reward` of type `R` (see [`DeterministicStateActionReward`](@ref
 mutable struct SimpleMDPEnv{T,R,S<:AbstractRNG}
     observation_space::DiscreteSpace
     action_space::DiscreteSpace
-    state::Int64
+    state::Int
     trans_probs::Array{T, 2}
     reward::R
-    initialstates::Array{Int64, 1}
-    isterminal::Array{Int64, 1}
+    initialstates::Array{Int, 1}
+    isterminal::Array{Int, 1}
     rng::S
 end
 
@@ -186,10 +186,10 @@ expected_rewards(r::NormalStateActionReward, ::Any) = r.mean
 
 # run SimpleMDPEnv
 """
-    run!(mdp::SimpleMDPEnv, action::Int64)
+    run!(mdp::SimpleMDPEnv, action::Int)
 Transition to a new state given `action`. Returns the new state.
 """
-function run!(mdp::SimpleMDPEnv, action::Int64)
+function run!(mdp::SimpleMDPEnv, action::Int)
     if mdp.isterminal[mdp.state] == 1
         reset!(mdp)
     else
@@ -199,9 +199,9 @@ function run!(mdp::SimpleMDPEnv, action::Int64)
 end
 
 """
-    run!(mdp::SimpleMDPEnv, policy::Array{Int64, 1}) = run!(mdp, policy[mdp.state])
+    run!(mdp::SimpleMDPEnv, policy::Array{Int, 1}) = run!(mdp, policy[mdp.state])
 """
-run!(mdp::SimpleMDPEnv, policy::Array{Int64, 1}) = run!(mdp, policy[mdp.state])
+run!(mdp::SimpleMDPEnv, policy::Array{Int, 1}) = run!(mdp, policy[mdp.state])
 
 
 function interact!(env::SimpleMDPEnv, action)

src/environments/classic_control/mountain_car.jl

@@ -8,17 +8,17 @@ struct MountainCarEnvParams{T}
     max_pos::T
     max_speed::T
     goal_pos::T
-    max_steps::Int64
+    max_steps::Int
 end
 
 mutable struct MountainCarEnv{T, R<:AbstractRNG} <: AbstractEnv
     params::MountainCarEnvParams{T}
     action_space::DiscreteSpace
     observation_space::MultiContinuousSpace{(2,), 1}
     state::Array{T, 1}
-    action::Int64
+    action::Int
     done::Bool
-    t::Int64
+    t::Int
     rng::R
 end
 

src/environments/classic_control/pendulum.jl

@@ -9,7 +9,7 @@ struct PendulumEnvParams{T}
     m::T
     l::T
     dt::T
-    max_steps::Int64
+    max_steps::Int
 end
 
 mutable struct PendulumEnv{T, R<:AbstractRNG} <: AbstractEnv
@@ -18,7 +18,7 @@ mutable struct PendulumEnv{T, R<:AbstractRNG} <: AbstractEnv
     observation_space::MultiContinuousSpace{(3,), 1}
     state::Array{T, 1}
     done::Bool
-    t::Int64
+    t::Int
     rng::R
 end
 

src/environments/hanabi.jl

@@ -91,8 +91,8 @@ mutable struct HanabiEnv <: AbstractEnv
     state::Base.RefValue{Hanabi.LibHanabi.PyHanabiState}
     moves::Vector{Base.RefValue{Hanabi.LibHanabi.PyHanabiMove}}
     observation_encoder::Base.RefValue{Hanabi.LibHanabi.PyHanabiObservationEncoder}
-    observation_space::MultiDiscreteSpace{Int64, 1}
-    action_space::DiscreteSpace{Int64}
+    observation_space::MultiDiscreteSpace{Int, 1}
+    action_space::DiscreteSpace{Int}
     reward::HanabiResult
 
     function HanabiEnv(;kw...)