Api change (#19)

* unify interfaces

* update screen after interact

* add minor comment

* add version check

* update docker

* revert to juali v1.2 due to https://github.com/JuliaLang/julia/pull/32408\#issuecomment-522168938

* update README

src/ReinforcementLearningEnvironments/.travis.yml

@@ -3,9 +3,7 @@ language: julia
 os:
   - linux
 julia:
-  - 1.0
-  - 1.1
-  - nightly
+  - 1.2
 notifications:
   email: false
 

src/ReinforcementLearningEnvironments/Dockerfile

@@ -1,4 +1,4 @@
-FROM julia:1.1
+FROM julia:1.2
 
 # install dependencies
 RUN set -eux; \

src/ReinforcementLearningEnvironments/README.md

@@ -7,7 +7,7 @@ This package serves as a one-stop place for different kinds of reinforcement lea
 Install:
 
 ```julia
-(v1.1) pkg> add https://github.com/JuliaReinforcementLearning/ReinforcementLearningEnvironments.jl
+pkg> add ReinforcementLearningEnvironments
 ```
 
 ## API
@@ -64,11 +64,11 @@ Take the `AtariEnv` for example:
 
 1. Install this package by:
     ```julia
-    (v1.1) pkg> add ReinforcementLearningEnvironments
+    pkg> add ReinforcementLearningEnvironments
     ```
 2. Install corresponding dependent package by:
     ```julia
-    (v1.1) pkg> add ArcadeLearningEnvironment
+    pkg> add ArcadeLearningEnvironment
     ```
 3. Using the above two packages:
     ```julia

src/ReinforcementLearningEnvironments/src/ReinforcementLearningEnvironments.jl

@@ -1,5 +1,8 @@
 module ReinforcementLearningEnvironments
 
+export RLEnvs
+const RLEnvs = ReinforcementLearningEnvironments
+
 using Reexport, Requires
 
 include("abstractenv.jl")

src/ReinforcementLearningEnvironments/src/abstractenv.jl

@@ -1,4 +1,4 @@
-export AbstractEnv, observe, reset!, interact!, action_space, observation_space, render
+export AbstractEnv, observe, reset!, interact!, action_space, observation_space, render, Observation, get_reward, get_terminal, get_state, get_legal_actions
 
 abstract type AbstractEnv end
 
@@ -7,4 +7,26 @@ function reset! end
 function interact! end
 function action_space end
 function observation_space end
-function render end
+function render end
+
+struct Observation{R, T, S, M<:NamedTuple}
+    reward::R
+    terminal::T
+    state::S
+    meta::M
+end
+
+Observation(;reward, terminal, state, kw...) = Observation(reward, terminal, state, merge(NamedTuple(), kw))
+
+get_reward(obs::Observation) = obs.reward
+get_terminal(obs::Observation) = obs.terminal
+get_state(obs::Observation) = obs.state
+get_legal_actions(obs::Observation) = obs.meta.legal_actions
+
+# !!! >= julia v1.3
+if VERSION >= v"1.3.0-rc1.0"
+    (env::AbstractEnv)(a) = interact!(env, a)
+end
+
+action_space(env::AbstractEnv) = env.action_space
+observation_space(env::AbstractEnv) = env.observation_space

src/ReinforcementLearningEnvironments/src/environments/atari.jl

@@ -2,19 +2,17 @@ using ArcadeLearningEnvironment, GR
 
 export AtariEnv
 
-struct AtariEnv{To,F} <: AbstractEnv
+mutable struct AtariEnv{To,F} <: AbstractEnv
     ale::Ptr{Nothing}
     screen::Array{UInt8, 1}
     getscreen!::F
-    actions::Array{Int32, 1}
+    actions::Array{Int64, 1}
     action_space::DiscreteSpace{Int}
     observation_space::To
     noopmax::Int
+    reward::Float32
 end
 
-action_space(env::AtariEnv) = env.action_space
-observation_space(env::AtariEnv) = env.observation_space
-
 """
     AtariEnv(name; colorspace = "Grayscale", frame_skip = 4, noopmax = 20,
                    color_averaging = true, repeat_action_probability = 0.)
@@ -51,24 +49,26 @@ function AtariEnv(name;
     end
     actions = actionset == :minimal ? getMinimalActionSet(ale) : getLegalActionSet(ale)
     action_space = DiscreteSpace(length(actions))
-    AtariEnv(ale, screen, getscreen!, actions, action_space, observation_space, noopmax)
+    AtariEnv(ale, screen, getscreen!, actions, action_space, observation_space, noopmax, 0.0f0)
 end
 
 function interact!(env::AtariEnv, a)
-    r = act(env.ale, env.actions[a])
+    env.reward = act(env.ale, env.actions[a])
     env.getscreen!(env.ale, env.screen)
-    (observation=env.screen, reward=r, isdone=game_over(env.ale))
+    nothing
 end
 
-function observe(env::AtariEnv)
-    env.getscreen!(env.ale, env.screen)
-    (observation=env.screen, isdone=game_over(env.ale))
-end
+observe(env::AtariEnv) = Observation(
+    reward = env.reward,
+    terminal = game_over(env.ale),
+    state = env.screen
+)
 
 function reset!(env::AtariEnv)
     reset_game(env.ale)
     for _ in 1:rand(0:env.noopmax) act(env.ale, Int32(0)) end
     env.getscreen!(env.ale, env.screen)
+    env.reward = 0.0f0  # dummy
     nothing
 end
 

src/ReinforcementLearningEnvironments/src/environments/classic_control/cart_pole.jl

@@ -42,9 +42,6 @@ function CartPoleEnv(; T = Float64, gravity = T(9.8), masscart = T(1.),
     cp
 end
 
-action_space(env::CartPoleEnv) = env.action_space
-observation_space(env::CartPoleEnv) = env.observation_space
-
 function reset!(env::CartPoleEnv{T}) where T <: Number
     env.state[:] = T(.1) * rand(env.rng, T, 4) .- T(.05)
     env.t = 0
@@ -53,7 +50,11 @@ function reset!(env::CartPoleEnv{T}) where T <: Number
     nothing
 end
 
-observe(env::CartPoleEnv) = (observation=env.state, isdone=env.done)
+observe(env::CartPoleEnv) = Observation(
+    reward = env.done ? 0.0 : 1.0,
+    terminal = env.done,
+    state = env.state
+)
 
 function interact!(env::CartPoleEnv{T}, a) where T <: Number
     env.action = a
@@ -76,7 +77,7 @@ function interact!(env::CartPoleEnv{T}, a) where T <: Number
     env.done = abs(env.state[1]) > env.params.xthreshold ||
                abs(env.state[3]) > env.params.thetathreshold ||
                env.t >= env.params.max_steps
-    (observation=env.state, reward=1., isdone=env.done)
+    nothing
 end
 
 function plotendofepisode(x, y, d)

src/ReinforcementLearningEnvironments/src/environments/classic_control/mdp.jl

@@ -9,46 +9,52 @@ export MDPEnv, POMDPEnv, SimpleMDPEnv, absorbing_deterministic_tree_MDP, stochas
 ##### POMDPEnv
 #####
 
-mutable struct POMDPEnv{T,Ts,Ta, R<:AbstractRNG}
+mutable struct POMDPEnv{T,Ts,Ta, R<:AbstractRNG} <: AbstractEnv
     model::T
     state::Ts
     actions::Ta
     action_space::DiscreteSpace
     observation_space::DiscreteSpace
+    observation::Int
+    reward::Float64
     rng::R
 end
 
-POMDPEnv(model; rng=Random.GLOBAL_RNG) = POMDPEnv(
-    model,
-    initialstate(model, rng),
-    actions(model),
-    DiscreteSpace(n_actions(model)),
-    DiscreteSpace(n_states(model)),
-    rng)
+function POMDPEnv(model; rng=Random.GLOBAL_RNG)
+    state = initialstate(model, rng)
+    as = DiscreteSpace(n_actions(model))
+    os = DiscreteSpace(n_states(model))
+    actions_of_model = actions(model)
+    s, o, r = generate_sor(model, state, actions_of_model[rand(as)], rng)
+    obs = observationindex(model, o)
+    POMDPEnv(model, state, actions_of_model, as, os, obs, 0., rng)
+end
 
 function interact!(env::POMDPEnv, action)
     s, o, r = generate_sor(env.model, env.state, env.actions[action], env.rng)
     env.state = s
-    (observation = observationindex(env.model, o),
-     reward = r,
-     isdone = isterminal(env.model, s))
+    env.reward = r
+    env.observation = observationindex(env.model, o)
+    nothing
 end
 
-function observe(env::POMDPEnv)
-    (observation = observationindex(env.model, generate_o(env.model, env.state, env.rng)),
-     isdone = isterminal(env.model, env.state))
-end
+observe(env::POMDPEnv) = Observation(
+    reward = env.reward,
+    terminal = isterminal(env.model, env.state),
+    state = env.observation
+)
 
 #####
 ##### MDPEnv
 #####
 
-mutable struct MDPEnv{T, Ts, Ta, R<:AbstractRNG}
+mutable struct MDPEnv{T, Ts, Ta, R<:AbstractRNG} <: AbstractEnv
     model::T
     state::Ts
     actions::Ta
     action_space::DiscreteSpace
     observation_space::DiscreteSpace
+    reward::Float64
     rng::R
 end
 
@@ -58,10 +64,9 @@ MDPEnv(model; rng=Random.GLOBAL_RNG) = MDPEnv(
     actions(model),
     DiscreteSpace(n_actions(model)),
     DiscreteSpace(n_states(model)),
-    rng)
-
-action_space(env::Union{MDPEnv, POMDPEnv}) = env.action_space
-observation_space(env::Union{MDPEnv, POMDPEnv}) = env.observation_space
+    0.,
+    rng
+)
 
 observationindex(env, o) = Int(o) + 1
 
@@ -74,15 +79,15 @@ function interact!(env::MDPEnv, action)
     s = rand(env.rng, transition(env.model, env.state, env.actions[action]))
     r = POMDPs.reward(env.model, env.state, env.actions[action])
     env.state = s
-    (observation = stateindex(env.model, s),
-     reward = r,
-     isdone = isterminal(env.model, s))
+    env.reward = r
+    nothing
 end
 
-function observe(env::MDPEnv)
-    (observation = stateindex(env.model, env.state),
-     isdone = isterminal(env.model, env.state))
-end
+observe(env::MDPEnv) = Observation(
+    reward = env.reward,
+    terminal = isterminal(env.model, env.state),
+    state = stateindex(env.model, env.state)
+)
 
 #####
 ##### SimpleMDPEnv
@@ -107,14 +112,15 @@ probabilities) `reward` of type `R` (see [`DeterministicStateActionReward`](@ref
 [`NormalStateActionReward`](@ref)), array of initial states
 `initialstates`, and `ns` - array of 0/1 indicating if a state is terminal.
 """
-mutable struct SimpleMDPEnv{T,R,S<:AbstractRNG}
+mutable struct SimpleMDPEnv{T,R,S<:AbstractRNG} <: AbstractEnv
     observation_space::DiscreteSpace
     action_space::DiscreteSpace
     state::Int
     trans_probs::Array{T, 2}
     reward::R
     initialstates::Array{Int, 1}
     isterminal::Array{Int, 1}
+    score::Float64
     rng::S
 end
 
@@ -125,12 +131,9 @@ function SimpleMDPEnv(ospace, aspace, state, trans_probs::Array{T, 2},
         reward = DeterministicStateActionReward(reward)
     end
     SimpleMDPEnv{T,typeof(reward),S}(ospace, aspace, state, trans_probs,
-                                     reward, initialstates, isterminal, rng)
+                                     reward, initialstates, isterminal, 0., rng)
 end
 
-observation_space(env::SimpleMDPEnv) = env.observation_space
-action_space(env::SimpleMDPEnv) = env.action_space
-
 # reward types
 """
     struct DeterministicNextStateReward
@@ -208,13 +211,15 @@ run!(mdp::SimpleMDPEnv, policy::Array{Int, 1}) = run!(mdp, policy[mdp.state])
 function interact!(env::SimpleMDPEnv, action)
     oldstate = env.state
     run!(env, action)
-    r = reward(env.rng, env.reward, oldstate, action, env.state)
-    (observation = env.state, reward = r, isdone = env.isterminal[env.state] == 1)
+    env.score = reward(env.rng, env.reward, oldstate, action, env.state)
+    nothing
 end
 
-function observe(env::SimpleMDPEnv)
-    (observation = env.state, isdone = env.isterminal[env.state] == 1)
-end
+observe(env::SimpleMDPEnv) = Observation(
+    reward = env.score,
+    terminal = env.isterminal[env.state] == 1,
+    state = env.state
+)
 
 function reset!(env::SimpleMDPEnv)
     env.state = rand(env.rng, env.initialstates)

src/ReinforcementLearningEnvironments/src/environments/classic_control/mountain_car.jl

@@ -50,11 +50,14 @@ function MountainCarEnv(; T = Float64, continuous = false,
     reset!(env)
     env
 end
+
 ContinuousMountainCarEnv(; kwargs...) = MountainCarEnv(; continuous = true, kwargs...)
 
-action_space(env::MountainCarEnv) = env.action_space
-observation_space(env::MountainCarEnv) = env.observation_space
-observe(env::MountainCarEnv) = (observation=env.state, isdone=env.done)
+observe(env::MountainCarEnv) = Observation(
+    reward = env.done ? 0. : -1.,
+    terminal = env.done,
+    state = env.state
+)
 
 function reset!(env::MountainCarEnv{A, T}) where {A, T}
     env.state[1] = .2 * rand(env.rng, T) - .6
@@ -78,7 +81,7 @@ function _interact!(env::MountainCarEnv, force)
                env.t >= env.params.max_steps
     env.state[1] = x
     env.state[2] = v
-    (observation=env.state, reward=-1., isdone=env.done)
+    nothing
 end
 
 # adapted from https://github.com/JuliaML/Reinforce.jl/blob/master/src/envs/mountain_car.jl