add an example

environments/environment.jl

@@ -179,8 +179,8 @@ function MeshCat.render(env::Environment,
     return nothing
 end
 
-function seed(env::Environment; s=0)
-    env.rng[1] = MersenneTwister(seed)
+function seed(env::Environment, s=0)
+    env.rng[1] = MersenneTwister(s)
     return nothing
 end
 
@@ -227,6 +227,7 @@ function contains(s::BoxSpace{T,N}, v::AbstractVector{T}) where {T,N}
 end
 
 # For compat with RLBase
+Base.length(s::BoxSpace) = s.n
 Base.in(v::AbstractVector{T}, s::BoxSpace{T,N}) where {T,N} = all(v .>= s.low) && all(v .<= s.high)
 Random.rand(rng::Random.AbstractRNG, s::BoxSpace{T,N}) where {T,N} = return rand(rng, T,N) .* (s.high .- s.low) .+ s.low
 

environments/rlenv.jl

@@ -23,7 +23,7 @@ RLBase.is_terminated(env::DojoRLEnv) = env.done
 
 RLBase.reset!(env::DojoRLEnv) = reset(env.dojoenv)
 
-RLBase.reward(env::DojoRLEnv) = error()
+RLBase.reward(env::DojoRLEnv) = env.reward
 RLBase.state(env::DojoRLEnv) = env.state
 
 Random.seed!(env::DojoRLEnv, seed) = Dojo.seed(env.dojoenv, seed)
@@ -33,7 +33,7 @@ Random.seed!(env::DojoRLEnv, seed) = Dojo.seed(env.dojoenv, seed)
 
 function (env::DojoRLEnv)(a)
     s, r, d, i = step(env.dojoenv, a)
-    env.state = s
+    env.state .= s
     env.reward = r
     env.done = d
     env.info = i

examples/deeprl/Project.toml

@@ -0,0 +1,5 @@
+[deps]
+Dojo = "ac60b53e-8d92-4c83-b960-e78698fa1916"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+ReinforcementLearning = "158674fc-8238-5cab-b5ba-03dfc80d1318"

examples/deeprl/ant_ppo.jl

@@ -0,0 +1,67 @@
+using ReinforcementLearning
+using Flux
+using Flux.Losses
+
+using Random
+using Dojo
+
+function RL.Experiment(
+    ::Val{:JuliaRL},
+    ::Val{:PPO},
+    ::Val{:DojoAnt},
+    ::Nothing,
+    save_dir = nothing,
+    seed = 42
+)
+    rng = MersenneTwister(seed)
+    N_ENV = 6
+    UPDATE_FREQ = 32
+    env_vec = [Dojo.DojoRLEnv("ant") for i in 1:N_ENV]
+    for i in 1:N_ENV
+        Random.seed!(env_vec[i], hash(seed+i))
+    end
+    env = MultiThreadEnv(env_vec; is_force=true)
+
+    ns, na = length(state(env[1])), length(action_space(env[1]))
+    RLBase.reset!(env)
+
+    agent = Agent(
+        policy = PPOPolicy(
+            approximator = ActorCritic(
+                actor = Chain(
+                    Dense(ns, 256, relu; init = glorot_uniform(rng)),
+                    Dense(256, na; init = glorot_uniform(rng)),
+                ),
+                critic = Chain(
+                    Dense(ns, 256, relu; init = glorot_uniform(rng)),
+                    Dense(256, 1; init = glorot_uniform(rng)),
+                ),
+                optimizer = ADAM(1e-3),
+            ),
+            γ = 0.99f0,
+            λ = 0.95f0,
+            clip_range = 0.1f0,
+            max_grad_norm = 0.5f0,
+            n_epochs = 4,
+            n_microbatches = 4,
+            actor_loss_weight = 1.0f0,
+            critic_loss_weight = 0.5f0,
+            entropy_loss_weight = 0.001f0,
+            update_freq = UPDATE_FREQ,
+        ),    
+        trajectory = PPOTrajectory(;
+            capacity = UPDATE_FREQ,
+            state = Matrix{Float32} => (ns, N_ENV),
+            action = Vector{Int} => (N_ENV,),
+            action_log_prob = Vector{Float32} => (N_ENV,),
+            reward = Vector{Float32} => (N_ENV,),
+            terminal = Vector{Bool} => (N_ENV,),
+        ),
+    )        
+    stop_condition = StopAfterStep(10_000, is_show_progress=!haskey(ENV, "CI"))
+    hook = TotalBatchRewardPerEpisode(N_ENV)
+    Experiment(agent, env, stop_condition, hook, "# PPO with Dojo Ant")        
+end
+
+ex = E`JuliaRL_PPO_DojoAnt`
+run(ex)