Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Nfq refactor #980

Merged
merged 4 commits into from
Sep 28, 2023
Merged
Show file tree
Hide file tree
Changes from 3 commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Original file line number Diff line number Diff line change
Expand Up @@ -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)),
Dense(ns, 32, σ; init=glorot_uniform(rng)),
Dense(32, 32, relu; init=glorot_uniform(rng)),
Dense(32, na; init=glorot_uniform(rng)),
),
optimiser=RMSProp()
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,
CasBex marked this conversation as resolved.
Show resolved Hide resolved
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
Expand Down
30 changes: 16 additions & 14 deletions src/ReinforcementLearningZoo/src/algorithms/dqns/NFQ.jl
Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,6 @@ Neural Fitted Q-iteration as implemented in [1]
[1] Riedmiller, M. (2005). Neural Fitted Q Iteration – First Experiences with a Data Efficient Neural Reinforcement Learning Method. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds) Machine Learning: ECML 2005. ECML 2005. Lecture Notes in Computer Science(), vol 3720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564096_32
"""
Base.@kwdef struct NFQ{A, R, F} <: AbstractLearner
action_space::AbstractVector
approximator::A
num_iterations::Integer = 20
epochs::Integer = 100
Expand All @@ -34,27 +33,30 @@ end

RLCore.forward(L::NFQ, s::AbstractArray) = RLCore.forward(L.approximator, s)

function RLCore.forward(learner::NFQ, env::AbstractEnv)
as = action_space(env)
return vcat(repeat(state(env), inner=(1, length(as))), transpose(as)) |> x -> send_to_device(device(learner.approximator), x) |> x->RLCore.forward(learner, x) |> send_to_host |> vec
function RLBase.optimise!(learner::NFQ, ::PostEpisodeStage, trajectory::Trajectory)
for batch in trajectory
optimise!(learner, batch)
end
end

function RLBase.optimise!(learner::NFQ, ::PostEpisodeStage, trajectory::Trajectory)
function RLBase.optimise!(learner::NFQ, batch::NamedTuple)
Q = learner.approximator
γ = learner.γ
loss_func = learner.loss_function
as = learner.action_space
las = length(as)
batch = ReinforcementLearningTrajectories.StatsBase.sample(trajectory)

(s, a, r, ss) = batch[[:state, :action, :reward, :next_state]]
a = Float32.(a)
s, a, r, ss = map(x->send_to_device(device(Q), x), (s, a, r, ss))
for i = 1:learner.num_iterations
(s, a, r, s′) = batch[[:state, :action, :reward, :next_state]]
a = CartesianIndex.(a, 1:length(a))
s, a, r, s′ = map(x->send_to_device(device(Q), x), (s, a, r, s′))
for _ = 1:learner.num_iterations
q′ = vec(maximum(RLCore.forward(Q, s′); dims=1))
G = r .+ γ .* q′
# Make an input x samples x |action space| array -- Q --> samples x |action space| -- max --> samples
G = r .+ γ .* (cat(repeat(ss, inner=(1, 1, las)), reshape(repeat(as, outer=(1, size(ss, 2))), (1, size(ss, 2), las)), dims=1) |> x -> maximum(RLCore.forward(Q, x), dims=3) |> vec)
for _ = 1:learner.epochs
Flux.train!((x, y) -> loss_func(RLCore.forward(Q, x), y), params(Q.model), [(vcat(s, a), transpose(G))], Q.optimiser)
gs = gradient(params(Q)) do
q = RLCore.forward(Q, s)[a]
loss_func(G, q)
end
RLBase.optimise!(Q, gs)
end
end
end
Loading