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_simulator_3d_static_circle import deep_mobile_printing_3d1r
PALN_CHOICE = 1 # 0 dense 1 sparse
PLAN_LIST = ["dense", "sparse"]
PLAN_NAME = PLAN_LIST[PALN_CHOICE]
config = Config()
config.seed = 5
config.environment = deep_mobile_printing_3d1r(plan_choose=PALN_CHOICE)
config.num_episodes_to_run = 5000
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:1"
config.overwrite_existing_results_file = True
config.randomise_random_seed = False
config.save_model = False
OUT_FILE_NAME = "SAC_3d_" + PLAN_NAME + "_seed_" + str(config.seed)
config.save_model_path = "/mnt/NAS/home/WenyuHan/SNAC/SAC/3D/static/" + OUT_FILE_NAME + "/"
config.file_to_save_data_results = "/mnt/NAS/home/WenyuHan/SNAC/SAC/3D/static/" + OUT_FILE_NAME + "/" + "Results_Data.pkl"
config.file_to_save_results_graph = "/mnt/NAS/home/WenyuHan/SNAC/SAC/3D/static/" + OUT_FILE_NAME + "/" + "Results_Graph.png"
if os.path.exists(config.save_model_path) == False:
os.makedirs(config.save_model_path)
config.hyperparameters = {
"Actor_Critic_Agents": {
"learning_rate": 0.005,
'SAC_Discrete': SAC_Discrete,
'DIAYN': DIAYN,
'DBH': DBH
}
if args.rts:
config.rts()
AGENTS = [DDQN, SAC_Discrete, DIAYN, DBH]
else:
AGENTS = [str_to_obj[i] for i in args.algorithms]
config.environment_name = args.environment
config.environment = gym.make(config.environment_name)
config.eval = args.evaluate
config.seed = args.seed
config.num_episodes_to_run = args.num_episodes
config.runs_per_agent = args.n_trials
config.use_GPU = args.use_GPU
config.save_results = args.save_results
config.run_prefix = args.run_prefix
config.train_existing_model = args.tem
config.save_directory = 'results/{}'.format(config.run_prefix)
if not os.path.exists(config.save_directory):
os.makedirs(config.save_directory)
config.visualise_overall_agent_results = True
config.standard_deviation_results = 1.0
linear_hidden_units = [128, 128, 32]
learning_rate = 0.01
buffer_size = 100000
batch_size = 256
batch_norm = False
