JuliaRL_PPO_CartPole
using ReinforcementLearning
using StableRNGs
using Flux
using Flux.Losses
function RL.Experiment(
::Val{:JuliaRL},
::Val{:PPO},
::Val{:CartPole},
::Nothing;
save_dir = nothing,
seed = 123,
)
rng = StableRNG(seed)
N_ENV = 8
UPDATE_FREQ = 32
env = MultiThreadEnv([
CartPoleEnv(; T = Float32, rng = StableRNG(hash(seed + i))) for i in 1:N_ENV
])
ns, na = length(state(env[1])), length(action_space(env[1]))
RLBase.reset!(env, is_force = true)
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),
) |> gpu,
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 CartPole")
end
using Plots
using Statistics
ex = E`JuliaRL_PPO_CartPole`
run(ex)
n = minimum(map(length, ex.hook.rewards))
m = mean([@view(x[1:n]) for x in ex.hook.rewards])
s = std([@view(x[1:n]) for x in ex.hook.rewards])
plot(m,ribbon=s)
Avg total reward per episode
┌────────────────────────────────────────┐
200 │⠀⠀⠀⠀⠀⠀⠀⡸⡇⣠⠀⡿⢿⠀⢸⠁⠀⠀⣔⠏⣧⡏⣧⡷⡰⡏⢳⢷⡏⡷⣇⡏⢹⣾⠉⠁⠀⠀⠀⠀│
│⠀⠀⠀⠀⠀⠀⠀⡇⢷⠁⢧⠃⠈⡆⢸⠀⡴⡜⡜⠀⣿⠃⢣⣷⢸⠀⠈⠸⠇⢷⡟⡇⢸⡹⠀⠀⠀⠀⠀⠀│
│⠀⠀⠀⠀⠀⠀⢸⠀⡈⢀⠈⢸⡇⡇⡸⢠⠇⢠⠃⠀⣿⠀⢸⢿⡼⠀⠀⠀⠀⠸⢱⡇⢸⡇⠀⠀⠀⠀⠀⠀│
│⠀⠀⠀⠀⠀⠀⢸⢸⣇⠏⣆⢸⣼⢱⡇⢸⢠⢸⠀⠀⣿⠀⠘⠘⠃⠀⠀⠀⠀⠀⢸⡇⠸⡇⠀⠀⠀⠀⠀⠀│
│⠀⠀⠀⠀⠀⠀⢸⢸⢻⠀⠀⢻⡏⡞⢻⠁⡿⡇⠀⠀⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⠇⠀⠀⠀⠀⠀⠀│
│⠀⠀⠀⠀⠀⠀⡎⢸⢀⢷⢰⢸⡇⢱⡸⠀⡇⠁⠀⠀⢻⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⠀⠀⠀⠀⠀⠀⠀⠀⠀│
│⠀⠀⠀⠀⠀⠀⣧⠎⢸⠀⠛⣼⢸⢀⠇⢸⠁⠀⠀⠀⠈⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│
Score │⠀⠀⠀⠀⠀⠀⣿⢠⠜⠀⠀⣿⠘⡾⡀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│
│⠀⠀⠀⠀⠀⢰⢹⢸⠀⠀⠀⣿⠀⠀⠟⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│
│⠀⠀⠀⠀⠀⡸⡎⢸⠀⠀⠀⠇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│
│⠀⠀⠀⢠⠎⠁⣇⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│
│⠀⠀⡄⢸⠀⢸⢸⡞⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│
│⠠⢼⣵⣼⠎⠁⡸⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│
│⠠⢜⢼⠏⠀⢀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│
0 │⠐⠃⠈⠑⠒⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│
└────────────────────────────────────────┘
0 70
Episode
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