mirror of
https://github.com/illiumst/marl-factory-grid.git
synced 2025-05-23 07:16:44 +02:00
97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
import pickle
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import warnings
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from typing import Union
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from os import PathLike
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from pathlib import Path
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import time
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import pandas as pd
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from stable_baselines3.common.callbacks import CallbackList
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from environments.factory.simple_factory import DirtProperties, SimpleFactory
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from environments.logging.monitor import MonitorCallback
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from environments.logging.plotting import prepare_plot
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from environments.logging.training import TraningMonitor
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warnings.filterwarnings('ignore', category=FutureWarning)
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warnings.filterwarnings('ignore', category=UserWarning)
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def combine_runs(run_path: Union[str, PathLike]):
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run_path = Path(run_path)
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df_list = list()
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for run, monitor_file in enumerate(run_path.rglob('monitor_*.pick')):
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with monitor_file.open('rb') as f:
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monitor_list = pickle.load(f)
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for m_idx in range(len(monitor_list)):
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monitor_list[m_idx]['episode'] = m_idx
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monitor_list[m_idx]['run'] = run
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df = pd.concat(monitor_list, ignore_index=True)
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df['train_step'] = range(df.shape[0])
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df = df.fillna(0)
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#for column in list(df.columns):
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# if column not in ['episode', 'run', 'step', 'train_step']:
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# if 'clean' in column or '_vs_' in column:
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# df[f'{column}_sum_roll'] = df[column].rolling(window=50, min_periods=1).sum()
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# else:
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# df[f'{column}_mean_roll'] = df[column].rolling(window=50, min_periods=1).mean()
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df_list.append(df)
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df = pd.concat(df_list, ignore_index=True)
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df = df.fillna(0).rename(columns={'episode': 'Episode', 'run': 'Run'})
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columns = [col for col in df.columns if col not in ['Episode', 'Run', 'train_step', 'step']]
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df_group = df.groupby(['Episode', 'Run']).aggregate(
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{col: 'mean' if col in ['dirt_amount', 'dirty_tiles'] else 'sum' for col in columns}
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)
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non_overlapp_window = df_group.groupby(['Run', (df_group.index.get_level_values('Episode') // 20)]).mean()
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df_melted = non_overlapp_window.reset_index().melt(id_vars=['Episode', 'Run'],
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value_vars=columns, var_name="Measurement",
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value_name="Score")
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prepare_plot(run_path / f'{run_path.name}_monitor_lineplot.png', df_melted)
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print('Plotting done.')
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if __name__ == '__main__':
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# combine_runs('debug_out/PPO_1622120377')
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# exit()
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from stable_baselines3 import PPO # DQN
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dirt_props = DirtProperties()
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time_stamp = int(time.time())
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out_path = None
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for seed in range(5):
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env = SimpleFactory(n_agents=1, dirt_properties=dirt_props, allow_diagonal_movement=True, allow_no_op=False)
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model = PPO("MlpPolicy", env, verbose=1, ent_coef=0.0, seed=seed, device='cpu')
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out_path = Path('debug_out') / f'{model.__class__.__name__}_{time_stamp}'
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identifier = f'{seed}_{model.__class__.__name__}_{time_stamp}'
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out_path /= identifier
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callbacks = CallbackList(
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[TraningMonitor(out_path / f'train_logging_{identifier}.csv'),
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MonitorCallback(env, filepath=out_path / f'monitor_{identifier}.pick', plotting=False)]
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)
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model.learn(total_timesteps=int(2e6), callback=callbacks)
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save_path = out_path / f'model_{identifier}.zip'
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save_path.parent.mkdir(parents=True, exist_ok=True)
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model.save(save_path)
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if out_path:
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combine_runs(out_path)
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