from utils.logging import default_logger as logger from utils.image import create_gif_subproc from utils.tensor_board import global_board from utils.helper_classes import Counter, Timer from utils.conf import Config from utils.save_env import SaveEnv from utils.prep import prep_args from env.walker.single_walker import BipedalWalker # definitions observe_dim = 24 action_dim = 4 # configs c = Config() #c.restart_from_trial = "2020_05_06_21_50_57" c.max_episodes = 5000 c.max_steps = 1000 c.replay_size = 500000 # or: explore_noise_params = [(0, 0.2)] * action_dim c.explore_noise_params = (0, 0.2) c.policy_noise_params = (0, 1.0, -0.5, 0.5) c.device = "cuda:0" c.root_dir = "/data/AI/tmp/multi_agent/walker/naive_ddpg_td3/" # train configs # lr: learning rate, int: interval # warm up should be less than one epoch c.ddpg_update_batch_size = 100
# -*- coding: utf-8 -*- """ Created on 2018.12.05 @author: zhangjun """ import tensorflow as tf from utils.model import load_model_predict, load_model_raw_predict from utils.conf import Config import glob config = Config(base_dir='../conf') def test_load_model_predict(): sess = tf.Session() feature_dict_example = {'col1': 2.0, 'col2': 3.0, 'col3': 5.0, 'col4': "a", 'col5': 3.0, 'col6': "r#w#k", 'col7': "f", 'col8': "e"} model_path_list = glob.glob(config.get_model_prop('model_export_dir') + '/*') export_path = max(model_path_list) p = load_model_predict(export_path=export_path, feature_dict=feature_dict_example,
from utils.image import create_gif_subproc from utils.tensor_board import global_board from utils.helper_classes import Counter, Timer from utils.conf import Config from utils.save_env import SaveEnv from utils.prep import prep_args from utils.parallel import get_context, Pool from env.walker.carrier import BipedalMultiCarrier # definitions observe_dim = 28 action_dim = 4 # configs c = Config() # c.restart_from_trial = "2020_05_09_15_00_31" c.max_episodes = 50000 c.max_steps = 1000 c.replay_size = 50000 c.agent_num = 1 c.device = "cuda:0" c.root_dir = "/data/AI/tmp/multi_agent/mcarrier/naive_ppo_parallel/" # train configs # lr: learning rate, int: interval c.workers = 5 c.discount = 0.99 c.learning_rate = 3e-4 c.entropy_weight = None
from utils.logging import default_logger as logger from utils.image import create_gif_subproc from utils.tensor_board import global_board from utils.helper_classes import Counter, Timer from utils.conf import Config from utils.save_env import SaveEnv from utils.prep import prep_args from env.walker.carrier import BipedalMultiCarrier # definitions observe_dim = 28 action_dim = 4 # configs c = Config() # c.restart_from_trial = "2020_05_06_21_50_57" c.max_episodes = 20000 c.max_steps = 2000 c.replay_size = 500000 c.agent_num = 3 c.sub_policy_num = 1 c.explore_noise_params = (0, 0.2) c.q_increase_rate = 1 c.q_decrease_rate = 1 c.device = "cuda:0" c.root_dir = "/data/AI/tmp/multi_agent/mcarrier/maddpg/" # train configs # lr: learning rate, int: interval
from utils.logging import default_logger as logger from utils.tensor_board import global_board from utils.helper_classes import Counter, Timer, Object from utils.conf import Config from utils.save_env import SaveEnv from utils.prep import prep_args from utils.parallel import get_context, Pool, mark_static_module from env.magent_helper import * from .utils import draw_agent_num_figure mark_static_module(magent) # configs c = Config() c.map_size = 50 c.agent_ratio = 0.04 agent_num = int(np.sqrt(c.map_size * c.map_size * c.agent_ratio)) ** 2 c.neighbor_num = 3 # c.restart_from_trial = "2020_05_09_15_00_31" c.max_episodes = 5000 c.max_steps = 500 c.replay_size = 20000 c.device = "cuda:0" c.storage_device = "cpu" c.root_dir = "/data/AI/tmp/multi_agent/magent/naive_ppo_parallel/"
from utils.logging import default_logger as logger from utils.image import create_gif_subproc from utils.tensor_board import global_board from utils.helper_classes import Counter, Timer from utils.conf import Config from utils.save_env import SaveEnv from utils.prep import prep_args from env.walker.single_walker import BipedalWalker # definitions observe_dim = 24 action_dim = 4 # configs c = Config() c.max_episodes = 5000 c.max_steps = 1000 c.replay_size = 500000 c.explore_noise_params = (0, 0.2) c.device = "cuda:0" c.root_dir = "/data/AI/tmp/multi_agent/walker/naive_ddpg/" c.ddpg_update_batch_size = 100 if __name__ == "__main__": save_env = SaveEnv(c.root_dir, restart_use_trial=c.restart_from_trial) prep_args(c, save_env) # save_env.remove_trials_older_than(diff_hour=1)
from models.frames.algorithms.hddpg import HDDPG from models.naive.env_magent_ddpg import Actor, Critic from utils.logging import default_logger as logger from utils.tensor_board import global_board from utils.helper_classes import Counter, Timer, Object from utils.conf import Config from utils.save_env import SaveEnv from utils.prep import prep_args from env.magent_helper import * from .utils import draw_agent_num_figure # configs c = Config() c.map_size = 50 c.agent_ratio = 0.04 c.neighbor_num = 3 agent_num = int(np.sqrt(c.map_size * c.map_size * c.agent_ratio))**2 #c.restart_from_trial = "2020_05_06_21_50_57" c.max_episodes = 20000 c.max_steps = 2000 c.replay_size = 500000 c.agent_num = 3 c.q_increase_rate = 1 c.q_decrease_rate = 1 c.device = "cuda:0"
from utils.tensor_board import global_board, normalize_seq_length from utils.helper_classes import Counter, Timer from utils.conf import Config from utils.save_env import SaveEnv from utils.prep import prep_args from utils.gym_env import disable_view_window # definitions env_name = "LunarLander-v2" env = gym.make(env_name) disable_view_window() observe_dim = env.observation_space.shape[0] action_dim = 4 # configs c = Config() # c.restart_from_trial = "2020_05_09_15_00_31" c.max_episodes = 50000 c.max_steps = 300 c.replay_size = 10000 c.device = "cuda:0" c.root_dir = "/data/AI/tmp/multi_agent/lunar_lander/naive_ppo/" # train configs # lr: learning rate, int: interval c.discount = 0.99 c.learning_rate = 1e-3 c.entropy_weight = 1e-2 c.ppo_update_batch_size = 100 c.ppo_update_times = 4
# -*- coding: utf-8 -*- """ Created on 2018.12.05 @author: zhangjun """ import tensorflow as tf from utils.util import list_files from utils.conf import Config from utils.data import dataProcess from utils.feature import map_more_feature config = Config(base_dir='../conf') def input_fn(data_path, num_epochs, mode, batch_size): sequence_cols = config.SEQUENCE_COLS def squence_split(raw_features): if len(sequence_cols) > 0: for col, sep in sequence_cols: raw_features = several_values_columns_to_array(raw_features, col, sep) return raw_features def several_values_columns_to_array(raw_features, feature_name, sep): raw_features[feature_name] = tf.sparse_tensor_to_dense( tf.string_split(raw_features[feature_name], sep), default_value='')