Python Config.randomise_random_seed Examples

Example #1

File: Test_HIRO.py Project: hyzcn/Deep-Reinforcement-Learning-Algorithms-with-PyTorch

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": {
        "max_lower_level_timesteps": 3,
        "Actor": {
            "learning_rate": 0.001,
            "linear_hidden_units": [20, 20],
            "final_layer_activation": "TANH",
            "batch_norm": False,
            "tau": 0.005,
            "gradient_clipping_norm": 5
        },
        "Critic": {
            "learning_rate": 0.01,

Example #2

File: Test_DQN_HER.py Project: hyzcn/Deep-Reinforcement-Learning-Algorithms-with-PyTorch

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.hyperparameters = {

    "DQN_Agents": {

        "learning_rate": 0.005,
        "batch_size": 3,
        "buffer_size": 40000,
        "epsilon": 0.1,
        "epsilon_decay_rate_denominator": 200,
        "discount_rate": 0.99,
        "tau": 0.1,
        "alpha_prioritised_replay": 0.6,
        "beta_prioritised_replay": 0.4,