def test_get_mean_and_standard_deviation_difference_results(): """Tests that get_mean_and_standard_deviation_difference_results method produces correct output""" results = [[1.0, 2.0, 3.0], [5.0, -33.0, 55.0], [2.5, 2.5, 2.5]] mean_results = [ np.mean([1.0, 5.0, 2.5]), np.mean([2.0, -33.0, 2.5]), np.mean([3.0, 55.0, 2.5]) ] std_results = [ np.std([1.0, 5.0, 2.5]), np.std([2.0, -33.0, 2.5]), np.std([3.0, 55.0, 2.5]) ] mean_minus_1_std = [ mean - std_val for mean, std_val in zip(mean_results, std_results) ] mean_plus_1_std = [ mean + std_val for mean, std_val in zip(mean_results, std_results) ] config = Config() config.standard_deviation_results = 1.0 trainer = Trainer(config, []) mean_minus_x_std_guess, mean_results_guess, mean_plus_x_std_guess = trainer.get_mean_and_standard_deviation_difference_results( results) assert mean_results == mean_results_guess assert mean_minus_1_std == mean_minus_x_std_guess assert mean_plus_1_std == mean_plus_x_std_guess config.standard_deviation_results = 3.0 trainer = Trainer(config, []) mean_minus_x_std_guess, mean_results_guess, mean_plus_x_std_guess = trainer.get_mean_and_standard_deviation_difference_results( results) mean_plus_3_std = [ mean + 3.0 * std_val for mean, std_val in zip(mean_results, std_results) ] mean_minus_3_std = [ mean - 3.0 * std_val for mean, std_val in zip(mean_results, std_results) ] assert mean_results == mean_results_guess assert mean_minus_3_std == mean_minus_x_std_guess assert mean_plus_3_std == mean_plus_x_std_guess
import gym, os from agents.hierarchical_agents.DIAYN import DIAYN from agents.hierarchical_agents.DBH import DBH from agents.actor_critic_agents.SAC_Discrete import SAC_Discrete from agents.actor_critic_agents.SAC import SAC from agents.DQN_agents.DDQN import DDQN from agents.Trainer import Trainer from utilities.data_structures.Config import Config import argparse config = Config() parser = argparse.ArgumentParser() parser.add_argument('--env', action='store', dest='environment', default='SpaceInvaders-v0', help='which environment to compare on') parser.add_argument('--alg', nargs='+', action='store', dest='algorithms', default='SAC_Discrete', help='which algorithms to compare') parser.add_argument('--eval', type=bool, default=False, action='store', dest='evaluate', help='set False for training and True for evaluating.') parser.add_argument('--num_ep', type=int,
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 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
from gym.wrappers import FlattenDictWrapper from agents.DQN_agents.DQN_HER import DQN_HER from environments.Bit_Flipping_Environment import Bit_Flipping_Environment 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 = Bit_Flipping_Environment(14) config.num_episodes_to_run = 4500 config.file_to_save_data_results = None #"Data_and_Graphs/Bit_Flipping_Results_Data.pkl" config.file_to_save_results_graph = None #"Data_and_Graphs/Bit_Flipping_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.hyperparameters = { "DQN_Agents": { "learning_rate": 0.001, "batch_size": 128, "buffer_size": 100000, "epsilon_decay_rate_denominator": 150, "discount_rate": 0.999, "incremental_td_error": 1e-8,
import pickle from Trainer import Trainer from utilities.data_structures.Config import Config trainer = Trainer(config=Config(), agents=None) # # trainer.visualise_set_of_preexisting_results(save_image_path="Four_Rooms_and_Long_Corridor.png", results_data_paths=["Long_Corridor_Results_Data.pkl", "Four_Rooms.pkl"], # plot_titles=["Long Corridor", "Four Rooms"], y_limits=[(0.0, 0.25), (-90.0, 100.25)]) trainer.visualise_preexisting_results( save_image_path="hrl_experiments/Taxi_graph_comparison.png", data_path="hrl_experiments/Taxi_data.pkl", title="Taxi v2", y_limits=(-800.0, 0.0)) # trainer.visualise_preexisting_results(save_image_path="Long_Corridor_Graph.png", data_path="Long_Corridor_Results_Data.pkl", # title="Long Corridor", y_limits=(0.0, 0.25)) # trainer.visualise_preexisting_results(save_image_path="Hopper_Results_Graph_Both_Agents.png", data_path="Hopper_Results_Data.pkl", # title="Hopper") #, y_limits=(0.0, 0.25)) # trainer.visualise_set_of_preexisting_results(results_data_paths=["Cart_Pole_Results_Data.pkl", # "Mountain_Car_Results_Data.pkl"], # plot_titles=["Cart Pole (Discrete Actions)", "Mountain Car (Continuous Actions)"], # save_image_path="CartPole_and_MountainCar_Graph.png") # trainer.visualise_set_of_preexisting_results(results_data_paths=["Data_and_Graphs/Bit_Flipping_Results_Data.pkl", # "Data_and_Graphs/Fetch_Reach_Results_Data.pkl"], # plot_titles=["Bit Flipping", "Fetch Reach"],
import gym import pytest from utilities.Utility_Functions import flatten_action_id_to_actions from utilities.data_structures.Config import Config config = Config() config.seed = 1 config.environment = gym.make("Taxi-v2") config.env_parameters = {} config.num_episodes_to_run = 1000 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.load_model = False linear_hidden_units = [10, 5] learning_rate = 0.01 buffer_size = 40000 batch_size = 256 batch_norm = False embedding_dimensionality = 15 gradient_clipping_norm = 5
import gym 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
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
import sys sys.path.insert(0, '../') from environments.isc_environments.SimpleISC import SimpleISC from utilities.data_structures.Config import Config from agents.Trainer import Trainer from agents.actor_critic_agents import A2C, A3C, DDPG, DDPG_HER from gym.core import Wrapper from torch.cuda import is_available config = Config() config.environment = Wrapper(SimpleISC(mode="DISCRETE")) config.num_episodes_to_run = 5 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 = False config.visualise_individual_results = False config.visualise_overall_agent_results = False config.standard_deviation_results = 1.0 config.runs_per_agent = 1 config.use_GPU = is_available() config.overwrite_existing_results_file = True config.randomise_random_seed = False config.save_model = False config.seed = 0 config.debug_mode = True config.wandb_log = True
from agents.DQN_agents.DDQN import DDQN from agents.actor_critic_agents.DDPG import DDPG from agents.actor_critic_agents.SAC import SAC from agents.actor_critic_agents.SAC_Discrete import SAC_Discrete from agents.actor_critic_agents.A3C import A3C from agents.DQN_agents.DDQN import DDQN from agents.DQN_agents.Dueling_DDQN import Dueling_DDQN from environments.VEC_Environment import VEC_Environment from agents.Trainer import Trainer from utilities.data_structures.Config import Config import matplotlib.pyplot as plt import numpy as np config = Config() config.seed = 1 config.num_episodes_to_run = 8000 # config.file_to_save_data_results = "results/data_and_graphs/VEC.pkl" # config.file_to_save_results_graph = "results/data_and_graphs/VEC.png" config.show_solution_score = False config.visualise_individual_results = False config.visualise_overall_agent_results = False 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 = False config.device = "cuda:0" config.hyperparameters = {
import gym from 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 from agents.hierarchical_agents.h_DQN import h_DQN config = Config() config.seed = 1 config.environment = gym.make("Taxi-v2") config.env_parameters = {} config.num_episodes_to_run = 10000 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 = { "HRL": { "linear_hidden_units": [10, 5], "learning_rate": 0.01,
from environments.isc_environments.SimpleISC import SimpleISC from utilities.data_structures.Config import Config from agents.Trainer import Trainer from agents.DQN_agents import DQN, DDQN, Dueling_DDQN, DDQN_With_Prioritised_Experience_Replay, DRQN import wandb from gym.core import Wrapper from torch.cuda import is_available config = Config() config.environment = Wrapper(SimpleISC(mode="DISCRETE")) config.num_episodes_to_run = 5_000 config.file_to_save_data_results = "results/data_and_graphs/isc/IllinoisSolarCar_Results_Data.pkl" config.runs_per_agent = 1 config.use_GPU = is_available() config.overwrite_existing_results_file = True config.randomise_random_seed = False config.save_model = False config.model = None config.seed = 0 config.debug_mode = True config.wandb_log = True config.wandb_job_type = "testing" config.wandb_entity = "rafael_piacsek" config.wandb_tags = ["initial testing"] config.wandb_model_log_freq = 1_000
import gym from agents.hierarchical_agents.DIAYN import DIAYN from agents.actor_critic_agents.SAC import SAC from agents.Trainer import Trainer from utilities.data_structures.Config import Config config = Config() config.environment = gym.make("MountainCarContinuous-v0") config.seed = 1 config.env_parameters = {} config.num_episodes_to_run = 10000 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 = True 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
"""Tests for the hierarchical RL agent HIRO""" import copy 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
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":
# import the agents and the trainer from agents.DQN_agents.DQN_multi_agent import DQN from agents.Trainer_multi_agent import Trainer routing_modes = [ "Q_routing_2_hop", "Q_routing_1_hop", "Q_routing_0_hop", "Q_routing", "TTSPWRR", "TTSP" ] network_names = ["5x6", "UES_Manhatan", "toronto"] gpu_num = int(sys.argv[1]) algorithm_num = int(sys.argv[2]) network_num = int(sys.argv[3]) config = Config() config.use_GPU = True assert (torch.cuda.is_available()) config.device = torch.device(gpu_num) config.routing_mode = routing_modes[algorithm_num] network_name = network_names[network_num] config.training_mode = True config.does_need_network_state = config.routing_mode in [ "Q_routing_2_hop", "Q_routing_1_hop", "Q_routing_0_hop" ] config.does_need_network_state_embeding = config.routing_mode in [ "Q_routing_2_hop", "Q_routing_1_hop"
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 environments.isc_environments.SimpleISC import SimpleISC 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
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,
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
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 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())
from agents.DQN_agents.DQN_HER import DQN_HER from agents.DQN_agents.DQN_With_Fixed_Q_Targets import DQN_With_Fixed_Q_Targets from agents.DQN_agents.Dueling_DDQN import Dueling_DDQN 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
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,
self.logger.info("Learning rate {}".format(new_lr)) 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
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_With_Fixed_Q_Targets import DQN_With_Fixed_Q_Targets from environments.Bit_Flipping_Environment import Bit_Flipping_Environment from agents.policy_gradient_agents.PPO import PPO 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 = 1 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.load_model = False config.hyperparameters = { "DQN_Agents": { "learning_rate": 0.005, "batch_size": 3,
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 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
# from environments.Bit_Flipping_Environment import Bit_Flipping_Environment # from agents.policy_gradient_agents.PPO import PPO # 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,
@author: kashishg """ 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