Modify Experiment

src/ReinforcementLearningExperiments/deps/experiments/experiments/DQN/JuliaRL_NFQ_CartPole.jl

@@ -21,54 +21,57 @@ function RLCore.Experiment(
     ::Val{:CartPole},
     seed = 123,
 )
+
     rng = StableRNG(seed)
     env = CartPoleEnv(; T=Float32, rng=rng)
-    ns, na = length(state(env)), length(first(action_space(env)))
+    ns, na = length(state(env)), length(action_space(env))
 
     agent = Agent(
         policy=QBasedPolicy(
             learner=NFQ(
-                action_space=action_space(env),
                 approximator=Approximator(
                     model=Chain(
-                        Dense(ns+na, 5, σ; init=glorot_uniform(rng)),
-                        Dense(5, 5, σ; init=glorot_uniform(rng)),
-                        Dense(5, 1; init=glorot_uniform(rng)),
-                    ),
-                    optimiser=RMSProp()
+                        Dense(ns, 32, σ; init=glorot_uniform(rng)),
+                        Dense(32, 32, relu; init=glorot_uniform(rng)),
+                        Dense(32, na; init=glorot_uniform(rng)),
+                    )|>gpu,
+                    optimiser=RMSProp(),
                 ),
                 loss_function=mse,
-                epochs=100,
+                epochs=10,
                 num_iterations=10,
                 γ = 0.95f0
             ),
             explorer=EpsilonGreedyExplorer(
                 kind=:exp,
                 ϵ_stable=0.001,
-                warmup_steps=500,
+                warmup_steps=1000,
+                decay_steps=3000,
                 rng=rng,
             ),
         ),
         trajectory=Trajectory(
             container=CircularArraySARTSTraces(
                 capacity=10_000,
                 state=Float32 => (ns,),
-                action=Float32 => (na,),
             ),
             sampler=BatchSampler{SS′ART}(
-                batch_size=128,
+                batch_size=2048,
                 rng=rng
             ),
             controller=InsertSampleRatioController(
-                threshold=100,
-                n_inserted=-1
+                threshold=1000,
+                ratio=1/10,
+                n_sampled=-1
             )
         )
     )
 
     stop_condition = StopAfterStep(10_000, is_show_progress=!haskey(ENV, "CI"))
     hook = TotalRewardPerEpisode()
+
     Experiment(agent, env, stop_condition, hook)
+
     end
 
 #+ tangle=false