c.model_save_int = 100 # in episodes
c.profile_int = 50 # in episodes
def policy_noise(action):
return t.clamp(
add_clipped_normal_noise_to_action(action, c.policy_noise_params), -1,
1)
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)
global_board.init(save_env.get_trial_train_log_dir())
writer = global_board.writer
logger.info("Directories prepared.")
actor = MW(
Actor(observe_dim, action_dim, 1).to(c.device), c.device, c.device)
actor_t = MW(
Actor(observe_dim, action_dim, 1).to(c.device), c.device, c.device)
critic = MW(
Critic(observe_dim, action_dim).to(c.device), c.device, c.device)
critic_t = MW(
Critic(observe_dim, action_dim).to(c.device), c.device, c.device)
critic2 = MW(
Critic(observe_dim, action_dim).to(c.device), c.device, c.device)
critic2_t = MW(
Critic(observe_dim, action_dim).to(c.device), c.device, c.device)
if is_warm_up:
# generate random actions
act_dim = env.get_action_space(group_handle)[0]
actions = np.random.randint(0, act_dim, agent_num, dtype=np.int32)
env.set_action(group_handle, actions)
if __name__ == "__main__":
total_steps = max_epochs * max_episodes * max_steps
# preparations
prep_dirs_default(root_dir)
logger.info("Directories prepared.")
global_board.init(log_dir + "train_log")
writer = global_board.writer
env = magent.GridWorld(generate_combat_config(map_size), map_size=map_size)
agent_num = int(np.sqrt(map_size * map_size * agent_ratio))**2
group1_handle, group2_handle = env.get_handles()
# shape: (act,)
action_dim = env.get_action_space(group1_handle)[0]
# shape: (view_width, view_height, n_channel)
view_space = env.get_view_space(group1_handle)
view_dim = np.prod(view_space)
# shape: (ID embedding + last action + last reward + relative pos)
feature_dim = env.get_feature_space(group1_handle)[0]
base_actor = SwarmActor(view_dim, action_dim, history_depth, neighbor_num,
logger.info("Framework 1 initialized")
c2.restart_from_trial = load_trial2
framework2 = create_models2()
logger.info("Framework 2 initialized")
operators = [(framework1, run_agents1, load_framework1),
(framework2, run_agents2, load_framework2)]
# testing
# preparations
config = generate_combat_config(map_size)
env = magent.GridWorld(config, map_size=map_size)
env.reset()
global_board.init(test_root_dir)
writer = global_board.writer
logger.info("Directories prepared.")
# begin training
episode = Counter()
episode_finished = False
wins = [0, 0]
while episode < max_episodes:
episode.count()
logger.info("Begin episode {} at {}".format(episode, dt.now().strftime("%m/%d-%H:%M:%S")))
# environment initialization
env.reset()
env.set_render_dir(test_root_dir)