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
def test_add_default_hyperparameters_if_not_overriden():
"""Tests that add_default_hyperparameters_if_not_overriden function works"""
config = Config()
default_hyperparameter_set = {'output_activation': 'None', 'hidden_activations': 'relu', 'dropout': 0.0, 'initialiser': 'default',
'batch_norm': False, 'columns_of_data_to_be_embedded': [], 'embedding_dimensions': [], 'y_range': (),
}
alternative_hyperparmater_set = {'output_activation': "YESSS!!", 'hidden_activations': 'relu', 'dropout': 0.0, 'initialiser': 'default',
'batch_norm': False, 'columns_of_data_to_be_embedded': [], 'embedding_dimensions': [], 'y_range': (),
"helo": 20}
config.hyperparameters = {"DQN_Agents": {}}
config.hyperparameters = Trainer(config, []).add_default_hyperparameters_if_not_overriden(config.hyperparameters)
assert config.hyperparameters == {"DQN_Agents": default_hyperparameter_set}
config.hyperparameters = {"DQN_Agents": {}, "Test": {}}
config.hyperparameters = Trainer(config, []).add_default_hyperparameters_if_not_overriden(config.hyperparameters)
assert config.hyperparameters == {"DQN_Agents": default_hyperparameter_set, "Test": default_hyperparameter_set}
config.hyperparameters = {"DQN_Agents": {"helo": 20, "output_activation": "YESSS!!"}}
config.hyperparameters = Trainer(config, []).add_default_hyperparameters_if_not_overriden(config.hyperparameters)
assert config.hyperparameters == {"DQN_Agents": alternative_hyperparmater_set}
"""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