import os
import sys
from os.path import dirname, abspath
sys.path.append(dirname(dirname(abspath(__file__))))
import gym
from agents.actor_critic_agents.SAC_Discrete import SAC_Discrete
from agents.Trainer import Trainer
from utilities.data_structures.Config import Config
from environments.DMP_Env_1D_dynamic import deep_mobile_printing_1d1r
config = Config()
config.seed = 1
config.environment = deep_mobile_printing_1d1r()
config.num_episodes_to_run = 10000
config.show_solution_score = False
config.visualise_individual_results = False
config.visualise_overall_agent_results = True
config.standard_deviation_results = 1.0
config.runs_per_agent = 1
config.use_GPU = True
config.GPU = "cuda:0"
config.overwrite_existing_results_file = True
config.randomise_random_seed = False
config.save_model = False
OUT_FILE_NAME = "SAC_1d" + "sin" + "_seed_" + str(config.seed)
config.save_model_path = "/mnt/NAS/home/WenyuHan/SNAC/SAC/1D/dynamic/" + OUT_FILE_NAME + "/"
config.file_to_save_data_results = "/mnt/NAS/home/WenyuHan/SNAC/SAC/1D/dynamic/" + OUT_FILE_NAME + "/" + "Results_Data.pkl"
config.file_to_save_results_graph = "/mnt/NAS/home/WenyuHan/SNAC/SAC/1D/dynamic/" + OUT_FILE_NAME + "/" + "Results_Graph.png"
if os.path.exists(config.save_model_path) == False:
os.makedirs(config.save_model_path)
from agents.policy_gradient_agents.REINFORCE import REINFORCE
from environments.FaceDiscreete import FaceEnvironementDiscreete
from agents.Trainer import Trainer
from utilities.data_structures.Config import Config
config = Config()
config.seed = 1
config.environment = FaceEnvironementDiscreete(
"../weights/blg_small_12_5e-06_5e-05_2_8_small_big_noisy_first_True_512")
config.num_episodes_to_run = 500
config.file_to_save_data_results = "Data_and_Graphs/FaceDiscreete.pkl"
config.file_to_save_results_graph = "Data_and_Graphs/FaceDiscreete.png"
config.show_solution_score = True
config.visualise_individual_results = True
config.visualise_overall_agent_results = True
config.standard_deviation_results = 1.0
config.runs_per_agent = 1
config.use_GPU = True
config.overwrite_existing_results_file = True
config.randomise_random_seed = True
config.save_model = True
actor_critic_agent_hyperparameters = {
"Actor": {
"learning_rate": 0.0003,
"linear_hidden_units": [64, 64],
"final_layer_activation": None,
"batch_norm": False,
