import sys from pathlib import Path from stable_baselines3.common.vec_env import SubprocVecEnv try: # noinspection PyUnboundLocalVariable if __package__ is None: DIR = Path(__file__).resolve().parent sys.path.insert(0, str(DIR.parent)) __package__ = DIR.name else: DIR = None except NameError: DIR = None pass import simplejson from environments.logging.recorder import EnvRecorder from environments import helpers as h from environments.factory.factory_dirt import DirtFactory from environments.factory.dirt_util import DirtProperties from environments.factory.factory_item import ItemFactory from environments.factory.additional.item.item_util import ItemProperties from environments.logging.envmonitor import EnvMonitor from environments.utility_classes import MovementProperties, ObservationProperties, AgentRenderOptions """ In this studie, we want to export trained Agents for debugging purposes. """ def encapsule_env_factory(env_fctry, env_kwrgs): def _init(): with env_fctry(**env_kwrgs) as init_env: return init_env return _init def load_model_run_baseline(policy_path, env_to_run): # retrieve model class model_cls = h.MODEL_MAP['A2C'] # Load both agent model = model_cls.load(policy_path / 'model.zip', device='cpu') # Load old env kwargs with next(policy_path.glob('*params.json')).open('r') as f: env_kwargs = simplejson.load(f) env_kwargs.update(done_at_collision=True) # Init Env with env_to_run(**env_kwargs) as env_factory: monitored_env_factory = EnvMonitor(env_factory) recorded_env_factory = EnvRecorder(monitored_env_factory) # Evaluation Loop for i in range(n Episodes) for episode in range(5): env_state = recorded_env_factory.reset() rew, done_bool = 0, False while not done_bool: action = model.predict(env_state, deterministic=True)[0] env_state, step_r, done_bool, info_obj = recorded_env_factory.step(action) rew += step_r if done_bool: break print(f'Factory run {episode} done, reward is:\n {rew}') recorded_env_factory.save_run(filepath=policy_path / f'monitor.pick') recorded_env_factory.save_records(filepath=policy_path / f'recorder.json') if __name__ == '__main__': # What to do: train = True individual_run = True combined_run = False multi_env = False train_steps = 2e6 frames_to_stack = 3 # Define a global studi save path study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}' def policy_model_kwargs(): return dict() # Define Global Env Parameters # Define properties object parameters obs_props = ObservationProperties(render_agents=AgentRenderOptions.NOT, additional_agent_placeholder=None, omit_agent_self=True, frames_to_stack=frames_to_stack, pomdp_r=2, cast_shadows=True) move_props = MovementProperties(allow_diagonal_movement=True, allow_square_movement=True, allow_no_op=False) dirt_props = DirtProperties(initial_dirt_ratio=0.35, initial_dirt_spawn_r_var=0.1, clean_amount=0.34, max_spawn_amount=0.1, max_global_amount=20, max_local_amount=1, spawn_frequency=0, max_spawn_ratio=0.05, dirt_smear_amount=0.0, agent_can_interact=True) item_props = ItemProperties(n_items=10, spawn_frequency=30, n_drop_off_locations=2, max_agent_inventory_capacity=15) factory_kwargs = dict(n_agents=1, max_steps=500, parse_doors=True, level_name='rooms', doors_have_area=True, verbose=False, mv_prop=move_props, obs_prop=obs_props, done_at_collision=False ) # Bundle both environments with global kwargs and parameters env_map = {} env_map.update({'dirt': (DirtFactory, dict(dirt_prop=dirt_props, **factory_kwargs.copy()))}) env_map.update({'item': (ItemFactory, dict(item_prop=item_props, **factory_kwargs.copy()))}) # env_map.update({'dest': (DestFactory, dict(dest_prop=dest_props, # **factory_kwargs.copy()))}) env_names = list(env_map.keys()) # Train starts here ############################################################ # Build Major Loop parameters, parameter versions, Env Classes and models if train: for env_key in (env_key for env_key in env_map if 'combined' != env_key): model_cls = h.MODEL_MAP['A2C'] combination_path = study_root_path / env_key env_class, env_kwargs = env_map[env_key] # Output folder if (combination_path / 'monitor.pick').exists(): continue combination_path.mkdir(parents=True, exist_ok=True) if not multi_env: env_factory = encapsule_env_factory(env_class, env_kwargs)() else: env_factory = SubprocVecEnv([encapsule_env_factory(env_class, env_kwargs) for _ in range(6)], start_method="spawn") param_path = combination_path / f'env_params.json' try: env_factory.env_method('save_params', param_path) except AttributeError: env_factory.save_params(param_path) # EnvMonitor Init callbacks = [EnvMonitor(env_factory)] # Model Init model = model_cls("MlpPolicy", env_factory, **policy_model_kwargs(), verbose=1, seed=69, device='cpu') # Model train model.learn(total_timesteps=int(train_steps), callback=callbacks) # Model save save_path = combination_path / f'model.zip' model.save(save_path) # Monitor Save callbacks[0].save_run(combination_path / 'monitor.pick') # Better be save then sorry: Clean up! del env_factory, model import gc gc.collect() # Train ends here ############################################################ # Evaluation starts here ##################################################### # First Iterate over every model and monitor "as trained" if individual_run: print('Start Individual Recording') for env_key in (env_key for env_key in env_map if 'combined' != env_key): # For trained policy in study_root_path / _identifier policy_path = study_root_path / env_key load_model_run_baseline(policy_path, env_map[policy_path.name][0]) # for policy_path in (y for y in policy_path.iterdir() if y.is_dir()): # load_model_run_baseline(policy_path) print('Done Individual Recording')