Simplify code structure

Project.toml

@@ -1,23 +1,19 @@
 name = "ReinforcementLearningBase"
 uuid = "e575027e-6cd6-5018-9292-cdc6200d2b44"
 authors = ["Johanni Brea <[email protected]>", "Jun Tian <[email protected]>"]
-version = "0.8.5"
+version = "0.9.0"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
 CommonRLInterface = "d842c3ba-07a1-494f-bbec-f5741b0a3e98"
+IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
 AbstractTrees = "0.3"
 CommonRLInterface = "0.2"
 MacroTools = "0.5"
 julia = "1.3"
-
-[extras]
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-
-[targets]
-test = ["Test"]

README.md

@@ -2,4 +2,43 @@
 
 [![Build Status](https://travis-ci.com/JuliaReinforcementLearning/ReinforcementLearningBase.jl.svg?branch=master)](https://travis-ci.com/JuliaReinforcementLearning/ReinforcementLearningBase.jl)
 
-ReinforcementLearningBase.jl holds the common types and utility functions to be shared by other components in ReinforcementLearning ecosystem.
+ReinforcementLearningBase.jl holds the common types and utility functions to be
+shared by other components in ReinforcementLearning ecosystem.
+
+
+## Examples
+
+<table>
+<th colspan="2">Traits</th><th> 1 </th><th> 2 </th><th> 3 </th><th> 4 </th><th> 5 </th><th> 6 </th><th> 7 </th><th> 8 </th><th> 9 </th><tr> <th rowspan="2"> ActionStyle </th><th> MinimalActionSet </th><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> </td> <td> ✔ </td><td> </td> <td> ✔ </td><td> ✔ </td><td> ✔ </td></tr>
+<tr> <th> FullActionSet </th><td> </td> <td> </td> <td> </td> <td> ✔ </td><td> </td> <td> ✔ </td><td> </td> <td> </td> <td> </td> </tr>
+<tr> <th rowspan="3"> ChanceStyle </th><th> Stochastic </th><td> ✔ </td><td> </td> <td> ✔ </td><td> ✔ </td><td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr>
+<tr> <th> Deterministic </th><td> </td> <td> ✔ </td><td> </td> <td> </td> <td> ✔ </td><td> ✔ </td><td> </td> <td> </td> <td> </td> </tr>
+<tr> <th> ExplicitStochastic </th><td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> ✔ </td><td> ✔ </td><td> ✔ </td></tr>
+<tr> <th rowspan="2"> DefaultStateStyle </th><th> Observation </th><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> </td> <td> ✔ </td><td> </td> </tr>
+<tr> <th> InformationSet </th><td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> ✔ </td><td> </td> <td> ✔ </td></tr>
+<tr> <th rowspan="2"> DynamicStyle </th><th> Simultaneous </th><td> </td> <td> </td> <td> </td> <td> </td> <td> ✔ </td><td> </td> <td> </td> <td> </td> <td> </td> </tr>
+<tr> <th> Sequential </th><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> </td> <td> ✔ </td><td> ✔ </td><td> ✔ </td><td> ✔ </td></tr>
+<tr> <th rowspan="2"> InformationStyle </th><th> PerfectInformation </th><td> </td> <td> ✔ </td><td> </td> <td> </td> <td> </td> <td> ✔ </td><td> </td> <td> ✔ </td><td> </td> </tr>
+<tr> <th> ImperfectInformation </th><td> ✔ </td><td> </td> <td> ✔ </td><td> ✔ </td><td> ✔ </td><td> </td> <td> ✔ </td><td> </td> <td> ✔ </td></tr>
+<tr> <th rowspan="2"> NumAgentStyle </th><th> MultiAgent </th><td> </td> <td> </td> <td> </td> <td> </td> <td> ✔ </td><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> ✔ </td></tr>
+<tr> <th> SingleAgent </th><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr>
+<tr> <th rowspan="2"> RewardStyle </th><th> TerminalReward </th><td> ✔ </td><td> ✔ </td><td> </td> <td> ✔ </td><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> ✔ </td></tr>
+<tr> <th> StepReward </th><td> </td> <td> </td> <td> ✔ </td><td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr>
+<tr> <th rowspan="3"> StateStyle </th><th> Observation </th><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> </td> <td> ✔ </td><td> </td> </tr>
+<tr> <th> InformationSet </th><td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> ✔ </td><td> </td> <td> ✔ </td></tr>
+<tr> <th> InternalState </th><td> </td> <td> </td> <td> ✔ </td><td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr>
+<tr> <th rowspan="4"> UtilityStyle </th><th> GeneralSum </th><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> ✔ </td><td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr>
+<tr> <th> ZeroSum </th><td> </td> <td> </td> <td> </td> <td> </td> <td> ✔ </td><td> ✔ </td><td> </td> <td> </td> <td> ✔ </td></tr>
+<tr> <th> ConstantSum </th><td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> ✔ </td><td> </td> </tr>
+<tr> <th> IdenticalUtility </th><td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> ✔ </td><td> </td> <td> </td> </tr>
+</table>
+<ol><li> <a href="https://github.com/JuliaReinforcementLearning/ReinforcementLearningBase.jl/tree/master/src/examples/MultiArmBanditsEnv.jl"> MultiArmBanditsEnv </a></li>
+<li> <a href="https://github.com/JuliaReinforcementLearning/ReinforcementLearningBase.jl/tree/master/src/examples/RandomWalk1D.jl"> RandomWalk1D </a></li>
+<li> <a href="https://github.com/JuliaReinforcementLearning/ReinforcementLearningBase.jl/tree/master/src/examples/TigerProblemEnv.jl"> TigerProblemEnv </a></li>
+<li> <a href="https://github.com/JuliaReinforcementLearning/ReinforcementLearningBase.jl/tree/master/src/examples/MontyHallEnv.jl"> MontyHallEnv </a></li>
+<li> <a href="https://github.com/JuliaReinforcementLearning/ReinforcementLearningBase.jl/tree/master/src/examples/RockPaperScissorsEnv.jl"> RockPaperScissorsEnv </a></li>
+<li> <a href="https://github.com/JuliaReinforcementLearning/ReinforcementLearningBase.jl/tree/master/src/examples/TicTacToeEnv.jl"> TicTacToeEnv </a></li>
+<li> <a href="https://github.com/JuliaReinforcementLearning/ReinforcementLearningBase.jl/tree/master/src/examples/TinyHanabiEnv.jl"> TinyHanabiEnv </a></li>
+<li> <a href="https://github.com/JuliaReinforcementLearning/ReinforcementLearningBase.jl/tree/master/src/examples/PigEnv.jl"> PigEnv </a></li>
+<li> <a href="https://github.com/JuliaReinforcementLearning/ReinforcementLearningBase.jl/tree/master/src/examples/KuhnPokerEnv.jl"> KuhnPokerEnv </a></li>
+</ol>

src/CommonRLInterface.jl

@@ -21,9 +21,9 @@ end
 const CommonRLEnvs = Union{CommonRLEnv,CommonRLMarkovEnv,CommonRLZeroSumEnv}
 
 function Base.convert(::Type{CRL.AbstractEnv}, env::AbstractEnv)
-    if get_num_players(env) == 1
+    if NumAgentStyle(env) === SINGLE_AGENT
         convert(CRL.AbstractMarkovEnv, env)
-    elseif get_num_players(env) == 2 && UtilityStyle(env) === ZERO_SUM
+    elseif NumAgentStyle(env) isa MultiAgent{2} && UtilityStyle(env) === ZERO_SUM
         convert(CRL.AbstractZeroSumEnv, env)
     else
         CommonRLEnv(env)
@@ -34,25 +34,25 @@ Base.convert(::Type{CRL.AbstractMarkovEnv}, env::AbstractEnv) = CommonRLMarkovEn
 Base.convert(::Type{CRL.AbstractZeroSumEnv}, env::AbstractEnv) = CommonRLZeroSumEnv(env)
 
 CRL.@provide CRL.reset!(env::CommonRLEnvs) = reset!(env.env)
-CRL.@provide CRL.actions(env::CommonRLEnvs) = get_actions(env.env)
-CRL.@provide CRL.observe(env::CommonRLEnvs) = get_state(env.env)
-CRL.state(env::CommonRLEnvs) = get_state(env.env)
+CRL.@provide CRL.actions(env::CommonRLEnvs) = action_space(env.env)
+CRL.@provide CRL.observe(env::CommonRLEnvs) = state(env.env)
+CRL.state(env::CommonRLEnvs) = state(env.env)
 CRL.provided(::typeof(CRL.state), env::CommonRLEnvs) =
     InformationStyle(env.env) === PERFECT_INFORMATION
-CRL.@provide CRL.terminated(env::CommonRLEnvs) = get_terminal(env.env)
-CRL.@provide CRL.player(env::CommonRLEnvs) = get_current_player(env.env)
+CRL.@provide CRL.terminated(env::CommonRLEnvs) = is_terminated(env.env)
+CRL.@provide CRL.player(env::CommonRLEnvs) = current_player(env.env)
 CRL.@provide CRL.clone(env::CommonRLEnvs) = CommonRLEnv(copy(env.env))
 
 CRL.@provide function CRL.act!(env::CommonRLEnvs, a)
     env.env(a)
-    get_reward(env.env)
+    reward(env.env)
 end
 
-CRL.valid_actions(x::CommonRLEnvs) = get_legal_actions(x.env)
+CRL.valid_actions(x::CommonRLEnvs) = legal_action_space(x.env)
 CRL.provided(::typeof(CRL.valid_actions), env::CommonRLEnvs) =
     ActionStyle(env.env) === FullActionSet()
 
-CRL.valid_action_mask(x::CommonRLEnvs) = get_legal_actions_mask(x.env)
+CRL.valid_action_mask(x::CommonRLEnvs) = legal_action_space_mask(x.env)
 CRL.provided(::typeof(CRL.valid_action_mask), env::CommonRLEnvs) =
     ActionStyle(env.env) === FullActionSet()
 
@@ -68,12 +68,12 @@ end
 Base.convert(::Type{AbstractEnv}, env::CRL.AbstractEnv) = convert(RLBaseEnv, env)
 Base.convert(::Type{RLBaseEnv}, env::CRL.AbstractEnv) = RLBaseEnv(env, 0.0f0)  # can not determine reward ahead. Assume `Float32`.
 
-get_state(env::RLBaseEnv) = CRL.observe(env.env)
-get_actions(env::RLBaseEnv) = CRL.actions(env.env)
-get_reward(env::RLBaseEnv) = env.r
-get_terminal(env::RLBaseEnv) = CRL.terminated(env.env)
-get_legal_actions(env::RLBaseEnv) = CRL.valid_actions(env.env)
-get_legal_actions_mask(env::RLBaseEnv) = CRL.valid_action_mask(env.env)
+state(env::RLBaseEnv) = CRL.observe(env.env)
+action_space(env::RLBaseEnv) = CRL.actions(env.env)
+reward(env::RLBaseEnv) = env.r
+is_terminated(env::RLBaseEnv) = CRL.terminated(env.env)
+legal_action_space(env::RLBaseEnv) = CRL.valid_actions(env.env)
+legal_action_space_mask(env::RLBaseEnv) = CRL.valid_action_mask(env.env)
 reset!(env::RLBaseEnv) = CRL.reset!(env.env)
 
 (env::RLBaseEnv)(a) = env.r = CRL.act!(env.env, a)

src/ReinforcementLearningBase.jl

@@ -3,10 +3,12 @@ module ReinforcementLearningBase
 const RLBase = ReinforcementLearningBase
 export RLBase
 
+using Random
+
 include("inline_export.jl")
 include("interface.jl")
-include("implementations/implementations.jl")
-include("base.jl")
 include("CommonRLInterface.jl")
+include("base.jl")
+include("examples/examples.jl")
 
 end # module