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)
import sys from os.path import dirname, abspath sys.path.append(dirname(dirname(abspath(__file__)))) import gym from agents.actor_critic_agents.A2C import A2C from agents.actor_critic_agents.A3C import A3C from agents.Trainer import Trainer from utilities.data_structures.Config import Config config = Config() config.seed = 1 config.environment = gym.make("gym_boxworld:boxworldRandomSmall-v0") config.num_episodes_to_run = int(1e3) config.file_to_save_data_results = "results/data_and_graphs/Boxworld_Results_Data.pkl" config.file_to_save_results_graph = "results/data_and_graphs/Boxworld_Results_Graph.png" 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 = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = True config.hyperparameters = { "Actor_Critic_Agents": {
from agents.DQN_agents.DDQN import DDQN from agents.hierarchical_agents.DIAYN import DIAYN from agents.hierarchical_agents.h_DQN import h_DQN from agents.hierarchical_agents.HIRO import HIRO from agents.hierarchical_agents.SNN_HRL import SNN_HRL from agents.policy_gradient_agents.PPO import PPO 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 = {
from A3C import A3C from agents.DQN_agents.DQN_HER import DQN_HER from DDQN import DDQN from environments.Four_Rooms_Environment import Four_Rooms_Environment from hierarchical_agents.DIAYN import DIAYN from hierarchical_agents.HRL import HRL from hierarchical_agents.SNN_HRL import SNN_HRL from agents.Trainer import Trainer from utilities.data_structures.Config import Config from agents.DQN_agents.DQN import DQN config = Config() config.seed = 1 config.environment = Four_Rooms_Environment( 15, 15, stochastic_actions_probability=0.25, random_start_user_place=True, random_goal_place=False) config.num_episodes_to_run = 200 config.file_to_save_data_results = "Data_and_Graphs/Four_Rooms.pkl" config.file_to_save_results_graph = "Data_and_Graphs/Four_Rooms.png" 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 = 3 config.use_GPU = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False
from A2C import A2C from Dueling_DDQN import Dueling_DDQN from SAC_Discrete import SAC_Discrete from agents.actor_critic_agents.A3C import A3C from agents.policy_gradient_agents.PPO import PPO from agents.Trainer import Trainer from utilities.data_structures.Config import Config from agents.DQN_agents.DDQN import DDQN from agents.DQN_agents.DDQN_With_Prioritised_Experience_Replay import DDQN_With_Prioritised_Experience_Replay from agents.DQN_agents.DQN import DQN from agents.DQN_agents.DQN_With_Fixed_Q_Targets import DQN_With_Fixed_Q_Targets config = Config() config.seed = 1 config.environment = gym.make("CartPole-v0") config.num_episodes_to_run = 450 config.file_to_save_data_results = "data_and_graphs/Cart_Pole_Results_Data.pkl" config.file_to_save_results_graph = "data_and_graphs/Cart_Pole_Results_Graph.png" 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 = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False config.hyperparameters = {
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_2D_dynamic import deep_mobile_printing_2d1r config = Config() config.seed = 1 # 0: sin # 1: gaussian # 2: step config.environment = deep_mobile_printing_2d1r(plan_choose=0) config.num_episodes_to_run = 5000 config.file_to_save_data_results = "results/data_and_graphs/Cart_Pole_Results_Data.pkl" config.file_to_save_results_graph = "results/data_and_graphs/Cart_Pole_Results_Graph.png" 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.overwrite_existing_results_file = True config.randomise_random_seed = True config.save_model = False config.hyperparameters = {
from environments.j2n6s300.DDPG_HER_env_Gazebo import j2n6s300_Environment from agents.actor_critic_agents.DDPG_HER import DDPG_HER from utilities.data_structures.Config import Config from agents.Trainer import Trainer from datetime import datetime import os now = datetime.now() # current date and time date_str = now.strftime("%Y-%m-%d_%H-%M-%S") os.mkdir('Data_and_Graphs/results_' + date_str) path = 'Data_and_Graphs/results_' + date_str + '/' config = Config() config.seed = 1 config.environment = j2n6s300_Environment(proxyID='Env1') config.num_episodes_to_run = 1 config.file_to_save_config = path + "config.json" config.file_to_save_data_results = path + "jaco_DDPG-HER.pkl" config.file_to_save_results_graph = path + "jaco_DDPG-HER.png" config.show_solution_score = False config.visualise_results_while_training = 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 = False config.randomise_random_seed = True config.load_model = False config.load_model_path = "Models/model.pt" config.save_model = True
from agents.hierarchical_agents.SNN_HRL import SNN_HRL from agents.Trainer import Trainer from utilities.data_structures.Config import Config from agents.DQN_agents.DQN import DQN from agents.hierarchical_agents.h_DQN import h_DQN from environments.Long_Corridor_Environment import Long_Corridor_Environment config = Config() config.seed = 1 config.env_parameters = {"stochasticity_of_action_right": 0.5} config.environment = Long_Corridor_Environment( stochasticity_of_action_right=config. env_parameters["stochasticity_of_action_right"]) config.num_episodes_to_run = 10000 config.file_to_save_data_results = "Data_and_Graphs/Long_Corridor_Results_Data.pkl" config.file_to_save_results_graph = "Data_and_Graphs/Long_Corridor_Results_Graph.png" 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 = 3 config.use_GPU = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False config.load_model = False config.hyperparameters = { "h_DQN": { "CONTROLLER": { "batch_size":
"clip_rewards":False } } num_vehicles = 30 num_episode = 10 trials = 100 action_type = ["random","greedy"] task_num = 32 task_file = "../sac/tasks.txt" # config.environment = VEC_Environment(num_vehicles=50, task_num=task_num) # config.environment.generate_change_tasks("../change_tasks.txt", 8) # with open(count_file,'w+') as f: # f.write("") config.environment = VEC_Environment(num_vehicles=num_vehicles, task_num=task_num) config.environment.load_offloading_tasks(task_file, 3) for iter in [5]: count_file = "../../../learningrate_{}.txt".format(iter) with open(count_file,'w+') as f: f.write("") config.environment.count_file = count_file for learning_rate in [0.00002, 0.00008,0.0002,0.0008,0.002,0.008]: print("num_vehicles=",num_vehicles) config.hyperparameters["Actor_Critic_Agents"]["Actor"]["learning_rate"]=learning_rate config.hyperparameters["Actor_Critic_Agents"]["Critic"]["learning_rate"]=learning_rate # for i in action_type: # print(i) # with open("../finish_count.txt",'a') as f: # f.write(i+'\n') # results = []
import os import sys sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__)))) import gym from agents.actor_critic_agents.DDPG import DDPG from agents.actor_critic_agents.DDPG_HER_Che import DDPG_HER_Che from utilities.data_structures.Config import Config from agents.Trainer import Trainer config = Config() config.seed = 1 config.environment = gym.make("FetchReach-v1") #config.environment = gym.make("FetchPush-v1") #config.environment = gym.make("FetchPickAndPlace-v1") #config.environment = gym.make("FetchSlide-v1") config.num_episodes_to_run = 2000 #config.num_episodes_to_run = 2000 config.file_to_save_data_results = None config.file_to_save_results_graph = None 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 = 3 config.runs_per_agent = 25 config.use_GPU = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False
from agents.actor_critic_agents.A2C import A2C from agents.DQN_agents.Dueling_DDQN import Dueling_DDQN from agents.actor_critic_agents.SAC_Discrete import SAC_Discrete from agents.actor_critic_agents.A3C import A3C from agents.policy_gradient_agents.PPO import PPO from agents.Trainer import Trainer from utilities.data_structures.Config import Config from agents.DQN_agents.DDQN import DDQN from agents.DQN_agents.DDQN_With_Prioritised_Experience_Replay import DDQN_With_Prioritised_Experience_Replay from agents.DQN_agents.DQN import DQN from agents.DQN_agents.DQN_With_Fixed_Q_Targets import DQN_With_Fixed_Q_Targets config = Config() config.seed = 1 config.environment = CartPoleEnv() #gym.make("CartPole-v0") config.num_episodes_to_run = 450 config.file_to_save_data_results = "results/data_and_graphs/Cart_Pole_Results_Data.pkl" config.file_to_save_results_graph = "results/data_and_graphs/Cart_Pole_Results_Graph.png" 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 = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False config.hyperparameters = { "DQN_Agents": {
str_to_obj = { 'SAC': SAC, 'DDQN': DDQN, '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]
""" from agents.DQN_agents.DQN_HER import DQN_HER from environments.j2n6s300.HER_env_tf import j2n6s300_Environment from agents.Trainer import Trainer from utilities.data_structures.Config import Config from datetime import datetime now = datetime.now() # current date and time num_episodes_to_run = 500 eps_decay_rate_denom = round(num_episodes_to_run/6) config = Config() config.seed = 1 config.environment = j2n6s300_Environment() config.num_episodes_to_run = num_episodes_to_run config.file_to_save_data_results = "Data_and_Graphs/{}jaco.pkl".format(now.strftime("%Y-%m-%d_%H-%M-%S_")) config.file_to_save_results_graph = "Data_and_Graphs/{}jaco.png".format(now.strftime("%Y-%m-%d_%H-%M-%S_")) config.show_solution_score = False config.visualise_results_while_training = True 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.overwrite_existing_results_file = False config.randomise_random_seed = True config.load_model = False config.load_model_path = "Models/.pt" config.save_model = False
import gym from environments.Atari_Environment import make_atari_game from agents.DQN_agents.DDQN import DDQN from agents.hierarchical_agents.HRL.HRL import HRL from agents.hierarchical_agents.HRL.Model_HRL import Model_HRL from agents.Trainer import Trainer from utilities.data_structures.Config import Config config = Config() config.seed = 1 config.environment = make_atari_game("SpaceInvaders-v0") config.env_parameters = {} config.num_episodes_to_run = 500 config.file_to_save_data_results = "data_and_graphs/hrl_experiments/Space_Invaders_Data.pkl" config.file_to_save_results_graph = "data_and_graphs/hrl_experiments/Space_Invaders.png" 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 = 10 config.use_GPU = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False # Loss is not drawing a random sample! otherwise wouldnt jump around that much!! linear_hidden_units = [32, 32] learning_rate = 0.005 # 0.001 taxi buffer_size = 1000000 batch_size = 256
import gym import sys sys.path.append('./') import agents from agents.policy_gradient_agents.PPO import PPO from agents.actor_critic_agents.DDPG import DDPG from agents.actor_critic_agents.SAC import SAC from agents.actor_critic_agents.TD3 import TD3 from agents.Trainer import Trainer from agents.hierarchical_agents.DIAYN import DIAYN from utilities.data_structures.Config import Config config = Config() config.seed = 1 config.environment = gym.make("HalfCheetah-v2") config.num_episodes_to_run = 1000 config.file_to_save_data_results = "data_and_graphs/Hopper_Results_Data.pkl" config.file_to_save_results_graph = "data_and_graphs/Hopper_Results_Graph.png" 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 = 3 config.use_GPU = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False actor_critic_agent_hyperparameters = { "Actor": { "learning_rate": 0.0003,
from utilities.data_structures.Config import Config from agents.DQN_agents.DQN import DQN config = Config() config.seed = 1 height = 15 width = 15 random_goal_place = False num_possible_states = (height * width)**(1 + 1 * random_goal_place) embedding_dimensions = [[num_possible_states, 20]] print("Num possible states ", num_possible_states) config.environment = Four_Rooms_Environment( height, width, stochastic_actions_probability=0.0, random_start_user_place=True, random_goal_place=random_goal_place) config.num_episodes_to_run = 1000 config.file_to_save_data_results = "Data_and_Graphs/Four_Rooms.pkl" config.file_to_save_results_graph = "Data_and_Graphs/Four_Rooms.png" 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 = 3 config.use_GPU = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False
from utilities.data_structures.Config import Config from agents.Trainer import Trainer from agents.DQN_agents.DQN import DQN from agents.DQN_agents.DDQN import DDQN from agents.DQN_agents.Dueling_DDQN import Dueling_DDQN from agents.DQN_agents.DDQN_With_Prioritised_Experience_Replay import DDQN_With_Prioritised_Experience_Replay from agents.DQN_agents.DRQN import DRQN from models.FCNN import FCNN from gym.core import Wrapper config = Config() config.environment = Wrapper(SimpleISC(mode="DISCRETE")) config.num_episodes_to_run = 50 config.file_to_save_data_results = "results/data_and_graphs/isc/IllinoisSolarCar_Results_Data.pkl" config.file_to_save_results_graph = "results/data_and_graphs/isc/IllinoisSolarCar_Results_Graph.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 = False config.overwrite_existing_results_file = True config.randomise_random_seed = False config.save_model = False config.seed = 0 config.debug_mode = True
sys.path.append( os.path.join(config.Constants['SUMO_PATH'], os.sep, 'tools')) sumoBinary = config.Constants["SUMO_GUI_PATH"] sumoCmd = [ sumoBinary, '-S', '-d', config.Constants['Simulation_Delay'], "-c", config.Constants["SUMO_CONFIG"], "--no-warnings", "true" ] traci.start(sumoCmd) init_traci() config.network = RoadNetworkModel(config.Constants["ROOT"], config.Constants["Network_XML"]) config.utils = Utils(config) config.environment = AR_SUMO_Environment(config) config.network_state_size = config.environment.utils.get_network_state_size() if config.routing_mode in ["Q_routing_1_hop", "Q_routing_2_hop"]: assert (config.network_state_size == num_GAT_features_per_layer[0]) gat = GAT( config=config, num_of_layers=config.hyperparameters["GAT"]['num_of_layers'], num_heads_per_layer=config.hyperparameters["GAT"]['num_heads_per_layer'], num_features_per_layer=config.hyperparameters["GAT"] ['num_features_per_layer'], add_skip_connection=config.hyperparameters["GAT"]['add_skip_connection'], bias=config.hyperparameters["GAT"]['bias'], dropout=config.hyperparameters["GAT"]['dropout'], log_attention_weights=not config.training_mode).to(config.device)
import gym import random import numpy as np import torch from hierarchical_agents.HIRO import HIRO from utilities.data_structures.Config import Config random.seed(1) np.random.seed(1) torch.manual_seed(1) config = Config() config.seed = 1 config.environment = gym.make("Pendulum-v0") config.num_episodes_to_run = 1500 config.file_to_save_data_results = None config.file_to_save_results_graph = None 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 = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False config.hyperparameters = { "LOWER_LEVEL": {
import gym from agents.hierarchical_agents.HRL.HRL import HRL from agents.Trainer import Trainer from utilities.data_structures.Config import Config config = Config() config.environment = gym.make("Taxi-v2") config.seed = 1 config.env_parameters = {} config.num_episodes_to_run = 2000 config.file_to_save_data_results = None config.file_to_save_results_graph = None 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 = 3 config.use_GPU = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False linear_hidden_units = [32, 32] learning_rate = 0.01 buffer_size = 100000 batch_size = 256 batch_norm = False embedding_dimensionality = 10 gradient_clipping_norm = 5 update_every_n_steps = 1
import gym from agents.policy_gradient_agents.PPO import PPO from agents.actor_critic_agents.DDPG import DDPG from agents.actor_critic_agents.SAC import SAC from agents.actor_critic_agents.TD3 import TD3 from agents.Trainer import Trainer from agents.hierarchical_agents.DIAYN import DIAYN from utilities.data_structures.Config import Config config = Config() config.seed = 1 config.environment = gym.make("Walker2d-v2") config.num_episodes_to_run = 400 config.file_to_save_data_results = "data_and_graphs/Walker_Results_Data.pkl" config.file_to_save_results_graph = "data_and_graphs/Walker_Results_Graph.png" 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 = 3 config.use_GPU = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False config.load_model = False actor_critic_agent_hyperparameters = { "Actor": { "learning_rate": 0.0003, "linear_hidden_units": [64, 64], "final_layer_activation": None,
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_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:
import gym from agents.policy_gradient_agents.PPO import PPO from agents.actor_critic_agents.DDPG import DDPG from agents.actor_critic_agents.SAC import SAC from agents.actor_critic_agents.TD3 import TD3 from agents.Trainer import Trainer from agents.hierarchical_agents.DIAYN import DIAYN from utilities.data_structures.Config import Config config = Config() config.seed = 1 config.environment = gym.make("Hopper-v2") config.num_episodes_to_run = 1000 config.file_to_save_data_results = "data_and_graphs/Hopper_Results_Data.pkl" config.file_to_save_results_graph = "data_and_graphs/Hopper_Results_Graph.png" 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 = 3 config.use_GPU = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False config.load_model = False actor_critic_agent_hyperparameters = { "Actor": { "learning_rate": 0.0003,
if __name__ == '__main__': from utilities.data_structures.Config import Config import gym ## envs import ## from environments.carla_enviroments import env_v1_ObstacleAvoidance # net = q_network_toa(n_action=4) # net.to('cuda') # input = torch.rand(size=(10, 3, 224, 224)).to('cuda') # q1, q2 = net(input) config = Config() config.seed = 1 config.environment = gym.make("ObstacleAvoidance-v0") config.num_episodes_to_run = 2000 config.file_to_save_data_results = "C:/my_project/Deep-Reinforcement-Learning-Algorithms-with-PyTorch/results/data_and_graphs/carla_obstacle_avoidance/data.pkl" config.file_to_save_results_graph = "C:/my_project/Deep-Reinforcement-Learning-Algorithms-with-PyTorch/results/data_and_graphs/carla_obstacle_avoidance/data.png" config.show_solution_score = False 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 = False config.randomise_random_seed = True config.save_model = True config.resume = False config.resume_path = ''
from utilities.data_structures.Config import Config from agents.DQN_agents.DDQN import DDQN from agents.DQN_agents.DDQN_With_Prioritised_Experience_Replay import DDQN_With_Prioritised_Experience_Replay from agents.DQN_agents.DQN import DQN from agents.DQN_agents.DQN_With_Fixed_Q_Targets import DQN_With_Fixed_Q_Targets from agents.policy_gradient_agents.REINFORCE import REINFORCE ## envs import ## from environments.carla_enviroments import env_v1_ObstacleAvoidance env_title = "ObstacleAvoidance-v0" config = Config() config.env_title = env_title config.seed = 1 config.environment = gym.make(env_title) config.num_episodes_to_run = 2000 config.show_solution_score = False 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 = False config.randomise_random_seed = True config.save_model = True config.log_loss = False config.log_base = time.strftime("%Y%m%d%H%M%S", time.localtime()) config.save_model_freq = 300 ## save model per 300 episodes config.retrain = False
import gym from agents.Trainer import Trainer from agents.actor_critic_agents.DDPG import DDPG from agents.hierarchical_agents.HIRO import HIRO from utilities.data_structures.Config import Config config = Config() config.seed = 1 config.environment = gym.make( "Reacher-v2") # Reacher-v2 "InvertedPendulum-v2") #Pendulum-v0 config.num_episodes_to_run = 1500 config.file_to_save_data_results = None config.file_to_save_results_graph = None 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 = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False config.load_model = False config.hyperparameters = { "HIRO": { "LOWER_LEVEL": { "max_lower_level_timesteps": 5, "Actor": { "learning_rate": 0.001, "linear_hidden_units": [20, 20], "final_layer_activation": "TANH",
import gym from models.Trainer import Trainer from models.actor_critic_agents.DDPG import DDPG from models.actor_critic_agents.TD3 import TD3 from models.policy_gradient_agents.PPO import PPO from utilities.data_structures.Config import Config config = Config() config.seed = 1 config.environment = gym.make("MountainCarContinuous-v0") config.num_episodes_to_run = 450 config.file_to_save_data_results = None config.file_to_save_results_graph = None 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 = 3 config.use_GPU = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False config.hyperparameters = { "Policy_Gradient_Agents": { "learning_rate": 0.05, "linear_hidden_units": [30, 15], "final_layer_activation": "TANH", "learning_iterations_per_round": 10, "discount_rate": 0.9,
# from environments.Four_Rooms_Environment import Four_Rooms_Environment # from agents.hierarchical_agents.SNN_HRL import SNN_HRL # from agents.actor_critic_agents.TD3 import TD3 from agents.Trainer import Trainer from utilities.data_structures.Config import Config from agents.DQN_agents.DQN import DQN import numpy as np import torch random.seed(1) np.random.seed(1) torch.manual_seed(1) config = Config() config.seed = 1 config.environment = Bit_Flipping_Environment(4) config.num_episodes_to_run = 2000 config.file_to_save_data_results = None config.file_to_save_results_graph = None config.visualise_individual_results = False config.visualise_overall_agent_results = False config.randomise_random_seed = False config.runs_per_agent = 1 config.use_GPU = False config.hyperparameters = { "DQN_Agents": { "learning_rate": 0.005, "batch_size": 64, "buffer_size": 40000, "epsilon": 0.1, "epsilon_decay_rate_denominator": 200,
# symbol="SH123038" # symbol="SH113561" # symbol="SH128096" # symbol="SH110058" # symbol="SZ128105" # symbol="SH128019" # symbol="SZ128112" # symbol="SH123052" # symbol="SH113553" # symbol="SH123032" # config.environment = gym.make('stocks-v0', frame_bound=(50, 100), window_size=10) # gym_anytrading.register_new('sz.000001') # gym_anytrading.register_new('sh.600959') print(symbol) gym_anytrading.register_new_kzz(symbol) config.environment = gym.make('kzz-v1') # config.environment.update_df() # column_list = ['turn', 'pctChg'] # column_list = ['turn', 'pctChg'] column_list = ["test2"] # column_list = ["turn", "pctChg", "peTTM", "psTTM", "pcfNcfTTM", "pbMRQ"] column_list_str = "_".join(column_list) # config.environment.update_df(fn=None, column_list=column_list) config.environment.update_df(fn=None, column_list=None) # config.environment.update_df(fn=lambda df:df.head(100), column_list=column_list) # config.environment = gym.make("CartPole-v0") config.num_episodes_to_run = 50 # config.num_episodes_to_run = 450 config.file_to_save_data_results = "results/data_and_graphs/stocks_Results_Data.pkl" config.file_to_save_results_graph = "results/data_and_graphs/stocks_Results_Graph.png" config.show_solution_score = False