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1846580--input.jl
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1846580--input.jl
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algo="DDPG"
# Parameters and architecture based on:
# https://github.com/msinto93/DDPG/blob/master/train.py
# https://github.com/JuliaReinforcementLearning/ReinforcementLearningZoo.jl/blob/master/src/experiments/rl_envs/JuliaRL_DDPG_Pendulum.jl
# https://github.com/FluxML/model-zoo/blob/master/contrib/games/differentiable-programming/pendulum/DDPG.jl
using Pkg
Pkg.activate(pwd())
using Flux, Printf, Zygote
using Flux.Optimise: update!
using BSON
using Statistics: mean, std, median
using DataStructures: CircularBuffer
using Distributions: sample, Normal, logpdf
using Random
using Reinforce
using Dates
using Plots
using CSV, DataFrames
gr()
#------------ local machine ----------
# Job_ID=1149869
# Task_ID = 1149869-1 #ENV["SGE_TASK_ID"]
# seed_run = 10 # run inference over all seeds
#--------cluster jobs------------
Job_ID = ENV["JOB_ID"]
Task_ID = ENV["SGE_TASK_ID"]
seed_run = parse(Int, Task_ID)
#-------------------------------- INPUTS --------------------------------------------
train = 0
plot_result = 0
plot_all = 0
render = 0
track = -0.7 # 0 - off, 1 - DRL, , rule-based percentage of start Soc e.g. 70% -> -0.7 (has to be negative)
season = "winter" # "all" "both" "summer" "winter"
price= "fix" # "fix", "TOU"
noise_type = "gn" # "ou", "pn", "gn", "en"
using Reinforce.ShemsEnv_U8: Shems
case = "$(season)_$(algo)_$(price)_base-256_gn.1_Env-U8-no-layer-norm-winter"
run = "test"
NUM_EP = 3_001 #50_000
L1 = 300 #256
L2 = 600 #256
idx=NUM_EP
test_every = 100
test_runs = 100
num_seeds = 40
#-------------------------------------
seed_ini = 123
# individual random seed for each run
rng_run = parse(Int, string(seed_ini)*string(seed_run))
start_time = now()
current_episode = 0
#--------------------------------- Memory ------------------------------------
BATCH_SIZE = 120 #100 # Yu: 120
MEM_SIZE = 24_000
MIN_EXP_SIZE = 24_000
########################################################################################
memory = CircularBuffer{Any}(MEM_SIZE)
#--------------------------------- Game environment ---------------------------
EP_LENGTH = Dict("train" => 24,
("summer", "eval") => 359, ("summer", "test") => 767,
("winter", "eval") => 359, ("winter", "test") => 719,
("both", "eval") => 719, ("both", "test") => 1487,
("all", "eval") => 1439, ("all", "test") => 2999) # length of whole evaluation set (different)
env_dict = Dict("train" => Shems(EP_LENGTH["train"], "data/$(season)_train_$(price).csv"),
"eval" => Shems(EP_LENGTH[season, "eval"], "data/$(season)_eval_$(price).csv"),
"test" => Shems(EP_LENGTH[season, "test"], "data/$(season)_test_$(price).csv"))
WAIT = Dict(
("summer", "DDPG") => 1500, ("summer", "TD3") => 1500, ("summer", "SAC") => 5000,
("winter", "DDPG") => 2000, ("winter", "TD3") => 2000, ("winter", "SAC") => 7000,
("both", "DDPG") => 3000, ("both", "TD3") => 3000, ("both", "SAC") => 10000,
("all", "DDPG") => 4000, ("all", "TD3") => 5000, ("all", "SAC") => 20_000)
# ----------------------------- Environment Parameters -------------------------
STATE_SIZE = length(env_dict["train"].state)
ACTION_SIZE = length(env_dict["train"].a)
ACTION_BOUND_HI = maximum(env_dict["train"].a)
ACTION_BOUND_LO = minimum(env_dict["train"].a)
# ACTION_BOUND_HI = Float32[1f0, 1f0, 1f0] #Float32(actions(env, env.state).hi[1])
# ACTION_BOUND_LO = Float32[-1f0, -1f0, -1f0] #Float32(actions(env, env.state).lo[1])
# ACTION_BOUND_HI = Float32[4.6f0, 3.0f0] #Float32(actions(env, env.state).hi[1])
# ACTION_BOUND_LO = Float32[-4.6f0, -3.0f0] #Float32(actions(env, env.state).lo[1])
#------------------------------- Action Noise --------------------------------
struct OUNoise
μ
σ
θ
dt
X
end
struct GNoise
μ
σ_act
σ_trg
end
mutable struct EpsNoise
ζ
ξ
ξ_min
end
mutable struct ParamNoise
μ
σ_current
σ_target
adoption
end
# Ornstein-Uhlenbeck / Gaussian Noise params
# based on: https://github.com/openai/baselines/blob/master/baselines/ddpg/noise.py
μ = 0f0 #mu
σ = 0.1f0 #0.2f0 #sigma
θ = 0.15f0 #theta
dt = 1f-2
# Epsilon Noise parameters based on Yu et al. 2019
ζ = 0.0005f0
ξ_0 = 0.5f0
ξ_min = 0.1f0
# Noise actor
noise_act = 0.1f0 #0.1f0 #2f-1
noise_trg = 0.2f0 #3f-1
# Fill struct with values
ou = OUNoise(μ, σ, θ, dt, zeros(Float32, ACTION_SIZE))
gn = GNoise(μ, noise_act, noise_trg)
en = EpsNoise(ζ, ξ_0, ξ_min)
pn = ParamNoise(μ, σ, noise_act, 1.01)