From b606aa1f2c8a3110ff070f8c519689c1f7e56f15 Mon Sep 17 00:00:00 2001
From: rejuvyesh <mail@rejuvyesh.com>
Date: Fri, 11 Mar 2022 01:13:22 +0000
Subject: [PATCH] cartpole is meaningless as no reward defined

---
 examples/deeprl/ant_ddpg.jl | 80 +++++++++++++++++++++++++++++++++++++
 1 file changed, 80 insertions(+)
 create mode 100644 examples/deeprl/ant_ddpg.jl

diff --git a/examples/deeprl/ant_ddpg.jl b/examples/deeprl/ant_ddpg.jl
new file mode 100644
index 000000000..e71d2844c
--- /dev/null
+++ b/examples/deeprl/ant_ddpg.jl
@@ -0,0 +1,80 @@
+using ReinforcementLearning
+using Flux
+using Flux.Losses
+
+using Random
+using Dojo
+
+function RL.Experiment(
+    ::Val{:JuliaRL},
+    ::Val{:DDPG},
+    ::Val{:DojoAnt},
+    ::Nothing,
+    save_dir = nothing,
+    seed = 42
+)
+
+    rng = MersenneTwister(seed)
+    env = Dojo.DojoRLEnv("ant")
+    Random.seed!(env, seed)
+    A = action_space(env)
+    ns, na = length(state(env)), length(action_space(env))
+    @show na
+    
+    init = glorot_uniform(rng)
+    
+    create_actor() = Chain(
+        Dense(ns, 30, relu; init = init),
+        Dense(30, 30, relu; init = init),
+        Dense(30, na, tanh; init = init),
+    )
+    create_critic() = Chain(
+        Dense(ns + na, 30, relu; init = init),
+        Dense(30, 30, relu; init = init),
+        Dense(30, 1; init = init),
+    )
+
+    agent = Agent(
+        policy = DDPGPolicy(
+            behavior_actor = NeuralNetworkApproximator(
+                model = create_actor(),
+                optimizer = ADAM(),
+            ),
+            behavior_critic = NeuralNetworkApproximator(
+                model = create_critic(),
+                optimizer = ADAM(),
+            ),
+            target_actor = NeuralNetworkApproximator(
+                model = create_actor(),
+                optimizer = ADAM(),
+            ),
+            target_critic = NeuralNetworkApproximator(
+                model = create_critic(),
+                optimizer = ADAM(),
+            ),
+            γ = 0.99f0,
+            ρ = 0.995f0,
+            na = na,
+            batch_size = 64,
+            start_steps = 1000,
+            start_policy = RandomPolicy(A; rng = rng),
+            update_after = 1000,
+            update_freq = 1,
+            act_limit = 1.0,
+            act_noise = 0.1,
+            rng = rng,
+        ),
+        trajectory = CircularArraySARTTrajectory(
+            capacity = 10000,
+            state = Vector{Float32} => (ns,),
+            action = Float32 => (na, ),
+        ),
+    )
+
+    stop_condition = StopAfterStep(10_000, is_show_progress=!haskey(ENV, "CI"))
+    hook = TotalRewardPerEpisode()
+    Experiment(agent, env, stop_condition, hook, "# Dojo Ant with DDPG")    
+end
+
+ex = E`JuliaRL_DDPG_DojoAnt`
+run(ex)
\ No newline at end of file