golden-sweep-12.py

import os
#import tensorflow as tf
import time
import stable_baselines3
from stable_baselines3.common.callbacks import BaseCallback
#from stable_baselines.bench import Monitor
from stable_baselines3.common.results_plotter import load_results, ts2xy

import gym
import numpy as np
import matplotlib.pyplot as plt
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
from stable_baselines3 import SAC
from torch import nn

from reset_ALGO2 import system1
from env5 import MCS
from typing import Tuple, Dict, Any, List, Optional
import csv
import json
import os
import wandb
K=8
T=3000
TaskTypes=6
t_interval=0.2
d_max=1000
d_min=200
W=1000000
i=0

pmax=0.1
score=[]



h, t_sense_distribution, E_sense_distribution, p_sense_distribution, throughput_distribution, required_sensors_per_task_distribution, E_harv, task_deadline_distribution, task_types_distribution, normalized_req_sensors_per_task, normalized_deadline_distribution, normalized_throughput_distribution, initial_battery_state, t_sense_distribution_ideal, average_channel_coeff_per_sensor, average_channel_gain, avg_SNR_linear = system1(
    K, T, TaskTypes, t_interval, d_max, d_min, W, i).create()
env = MCS(T, K, pmax, W, h, t_sense_distribution, t_sense_distribution_ideal, E_sense_distribution,
          p_sense_distribution,
          throughput_distribution, required_sensors_per_task_distribution, E_harv, task_deadline_distribution,
          task_types_distribution, normalized_req_sensors_per_task, normalized_deadline_distribution,
          normalized_throughput_distribution, initial_battery_state, average_channel_coeff_per_sensor,
          average_channel_gain, avg_SNR_linear, i)
log_dir = "golden_sweep_31.5"
os.makedirs(log_dir, exist_ok=True)
env = Monitor(env, log_dir)


model = SAC("MlpPolicy", env, ent_coef=0.0001, tensorboard_log=log_dir, verbose=1)


model.learn(30000000)
model.save("golden_sweep_12_31.5")

print('done')