added experiments to test

Author: Mytolo

src/ReinforcementLearningExperiments/Project.toml

@@ -6,12 +6,14 @@ version = "0.3.1"
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 ReinforcementLearningBase = "e575027e-6cd6-5018-9292-cdc6200d2b44"
 ReinforcementLearningCore = "de1b191a-4ae0-4afa-a27b-92d07f46b2d6"
 ReinforcementLearningEnvironments = "25e41dd2-4622-11e9-1641-f1adca772921"
 ReinforcementLearningZoo = "d607f57d-ee1e-4ba7-bcf2-7734c1e31854"
+Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Weave = "44d3d7a6-8a23-5bf8-98c5-b353f8df5ec9"
 
@@ -29,7 +31,9 @@ julia = "1.9"
 
 [extras]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
+Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["CUDA", "Test"]
+test = ["CUDA", "PyCall", "Test"]

src/ReinforcementLearningExperiments/src/experiments/MARL/DQN_mpe_simple.jl renamed to src/ReinforcementLearningExperiments/deps/experiments/experiments/MARL/DQN_mpe_simple.jl

@@ -17,7 +17,7 @@ using Flux.Losses: huber_loss
 function RLCore.Experiment(
     ::Val{:JuliaRL},
     ::Val{:DQN},
-    ::Val{:MPESimple};
+    ::Val{:MPESimple},
     seed=123,
     n=1,
     γ=0.99f0,

src/ReinforcementLearningExperiments/src/experiments/MARL/IDQN_TicTacToe.jl renamed to src/ReinforcementLearningExperiments/deps/experiments/experiments/MARL/IDQN_TicTacToe.jl

@@ -7,7 +7,6 @@
 # ---
 
 using StableRNGs
-using ReinforcementLearning
 using ReinforcementLearningBase
 using ReinforcementLearningZoo
 using ReinforcementLearningCore
@@ -16,7 +15,6 @@ using Flux
 using Flux.Losses: huber_loss
 using Flux: glorot_uniform
 
-using ProgressMeter
 
 
 rng = StableRNG(1234)
@@ -25,6 +23,71 @@ cap = 100
 
 RLCore.forward(L::DQNLearner, state::A) where {A <: Real} = RLCore.forward(L, [state])
 
+
+episodes_per_step = 25
+
+function RLCore.Experiment(
+    ::Val{:JuliaRL},
+    ::Val{:IDQN},
+    ::Val{:TicTacToe},
+    seed=123,
+    n=1,
+    γ=0.99f0,
+    is_enable_double_DQN=true
+)
+    rng = StableRNG(seed)
+    create_policy() = QBasedPolicy(
+            learner=DQNLearner(
+                approximator=Approximator(
+                    model=TwinNetwork(
+                        Chain(
+                            Dense(1, 512, relu; init=glorot_uniform(rng)),
+                            Dense(512, 256, relu; init=glorot_uniform(rng)),
+                            Dense(256, 9; init=glorot_uniform(rng)),
+                        );
+                        sync_freq=100
+                    ),
+                    optimiser=Adam(),
+                ),
+                n=n,
+                γ=γ,
+                is_enable_double_DQN=is_enable_double_DQN,
+                loss_func=huber_loss,
+                rng=rng,
+            ),
+            explorer=EpsilonGreedyExplorer(
+                kind=:exp,
+                ϵ_stable=0.01,
+                decay_steps=500,
+                rng=rng,
+            ),
+        )
+    
+    e  = TicTacToeEnv();
+    m = MultiAgentPolicy(NamedTuple((player =>
+                               Agent(player != :Cross ? create_policy() : RandomPolicy(;rng=rng),
+                                   Trajectory(
+                                       container=CircularArraySARTTraces(
+                                         capacity=cap,
+                                         state=Integer => (1,),
+                                       ),
+                                       sampler=NStepBatchSampler{SS′ART}(
+                                           n=n,
+                                           γ=γ,
+                                           batch_size=1,
+                                           rng=rng
+                                       ),
+                                       controller=InsertSampleRatioController(
+                                           threshold=1,
+                                           n_inserted=0
+                                       ))
+                               )
+                               for player in players(e)))
+                           );
+    hooks = MultiAgentHook(NamedTuple((p => TotalRewardPerEpisode() for p ∈ players(e))))
+    Experiment(m, e, StopAfterEpisode(episodes_per_step), hooks)
+end
+
 create_policy() = QBasedPolicy(
         learner=DQNLearner(
             approximator=Approximator(
@@ -36,7 +99,7 @@ create_policy() = QBasedPolicy(
                     );
                     sync_freq=100
                 ),
-                optimiser=ADAM(),
+                optimiser=Adam(),
             ),
             n=32,
             γ=0.99f0,
@@ -75,9 +138,8 @@ m = MultiAgentPolicy(NamedTuple((player =>
                        );
 hooks = MultiAgentHook(NamedTuple((p => TotalRewardPerEpisode() for p ∈ players(e))))
 
-episodes_per_step = 25
 win_rates = (Cross=Float64[], Nought=Float64[])
-@showprogress for i ∈ 1:2
+for i ∈ 1:2
     run(m, e, StopAfterEpisode(episodes_per_step; is_show_progress=false), hooks)
     wr_cross = sum(hooks[:Cross].rewards)/(i*episodes_per_step)
     wr_nought = sum(hooks[:Nought].rewards)/(i*episodes_per_step)

src/ReinforcementLearningExperiments/src/ReinforcementLearningExperiments.jl

@@ -19,6 +19,7 @@ include(joinpath(EXPERIMENTS_DIR, "JuliaRL_Rainbow_CartPole.jl"))
 include(joinpath(EXPERIMENTS_DIR, "JuliaRL_VPG_CartPole.jl"))
 include(joinpath(EXPERIMENTS_DIR, "JuliaRL_TRPO_CartPole.jl"))
 include(joinpath(EXPERIMENTS_DIR, "JuliaRL_MPO_CartPole.jl"))
+include(joinpath(EXPERIMENTS_DIR, "IDQN_TicTacToe.jl"))
 
 # dynamic loading environments
 function __init__() end

src/ReinforcementLearningExperiments/test/experiments/DQN_mpe_simple.jl

@@ -0,0 +1,86 @@
+
+# ---
+# title: JuliaRL\_DQN\_MPESimple
+# cover:
+# description: DQN applied to MPE simple
+# date: 2023-02-01
+# author: "[Panajiotis Keßler](mailto:panajiotis@christoforidis.net)"
+# ---
+
+using PyCall
+using ReinforcementLearningCore, ReinforcementLearningBase, ReinforcementLearningZoo
+using Flux
+using Flux: glorot_uniform
+
+using StableRNGs: StableRNG
+using Flux.Losses: huber_loss
+
+function RLCore.Experiment(
+    ::Val{:JuliaRL},
+    ::Val{:DQN},
+    ::Val{:MPESimple},
+    seed=123,
+    n=1,
+    γ=0.99f0,
+    is_enable_double_DQN=true
+)
+    rng = StableRNG(seed)
+    env = discrete2standard_discrete(PettingZooEnv("mpe.simple_v2"; seed=seed))
+    ns, na = length(state(env)), length(action_space(env))
+
+    agent = Agent(
+        policy=QBasedPolicy(
+            learner=DQNLearner(
+                approximator=Approximator(
+                    model=TwinNetwork(
+                        Chain(
+                            Dense(ns, 128, relu; init=glorot_uniform(rng)),
+                            Dense(128, 128, relu; init=glorot_uniform(rng)),
+                            Dense(128, na; init=glorot_uniform(rng)),
+                        );
+                        sync_freq=100
+                    ),
+                    optimiser=Adam(),
+                ),
+                n=n,
+                γ=γ,
+                is_enable_double_DQN=is_enable_double_DQN,
+                loss_func=huber_loss,
+                rng=rng,
+            ),
+            explorer=EpsilonGreedyExplorer(
+                kind=:exp,
+                ϵ_stable=0.01,
+                decay_steps=500,
+                rng=rng,
+            ),
+        ),
+        trajectory=Trajectory(
+            container=CircularArraySARTTraces(
+                capacity=1000,
+                state=Float32 => (ns,),
+            ),
+            sampler=NStepBatchSampler{SS′ART}(
+                n=n,
+                γ=γ,
+                batch_size=32,
+                rng=rng
+            ),
+            controller=InsertSampleRatioController(
+                threshold=100,
+                n_inserted=-1
+            )
+        )
+    )
+
+    stop_condition = StopAfterEpisode(150, is_show_progress=!haskey(ENV, "CI"))
+    hook = TotalRewardPerEpisode()
+    Experiment(agent, env, stop_condition, hook)
+end
+
+using Plots
+ex = E`JuliaRL_DQN_MPESimple`
+run(ex)
+plot(ex.hook.rewards)
+savefig("JuliaRL_DQN_MPESimple.png")
+

src/ReinforcementLearningExperiments/test/runtests.jl

@@ -1,6 +1,14 @@
 using ReinforcementLearningExperiments
 using CUDA
 
+using Requires
+
+const EXPERIMENTS_DIR = joinpath(@__DIR__, "experiments")
+@require PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0" include(
+    joinpath(EXPERIMENTS_DIR, "DQN_mpe_simple.jl")
+)
+
+
 CUDA.allowscalar(false)
 
 run(E`JuliaRL_NFQ_CartPole`)
@@ -15,6 +23,8 @@ run(E`JuliaRL_VPG_CartPole`)
 run(E`JuliaRL_MPODiscrete_CartPole`)
 run(E`JuliaRL_MPOContinuous_CartPole`)
 run(E`JuliaRL_MPOCovariance_CartPole`)
+@require PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0" run(E`JuliaRL_DQN_MPESimple`)
+run(E`JuliaRL_IDQN_TicTacToe`)
 # run(E`JuliaRL_BC_CartPole`)
 # run(E`JuliaRL_VMPO_CartPole`)
 # run(E`JuliaRL_BasicDQN_MountainCar`)