mirror of
https://github.com/illiumst/marl-factory-grid.git
synced 2025-05-22 23:06:43 +02:00
103 lines
3.4 KiB
Python
103 lines
3.4 KiB
Python
from collections import defaultdict
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from pathlib import Path
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from typing import Union
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import numpy as np
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import pandas as pd
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import simplejson
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from stable_baselines3.common.callbacks import BaseCallback
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from environments.factory.base.base_factory import REC_TAC
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class EnvRecorder(BaseCallback):
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def __init__(self, env, entities='all'):
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super(EnvRecorder, self).__init__()
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self.unwrapped = env
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self._recorder_dict = defaultdict(list)
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self._recorder_out_list = list()
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if isinstance(entities, str):
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if entities.lower() == 'all':
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self._entities = None
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else:
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self._entities = [entities]
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else:
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self._entities = entities
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self.started = False
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self.closed = False
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def __getattr__(self, item):
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return getattr(self.unwrapped, item)
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def reset(self):
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self.unwrapped._record_episodes = True
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return self.unwrapped.reset()
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def _on_training_start(self) -> None:
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self.unwrapped._record_episodes = True
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pass
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def _read_info(self, env_idx, info: dict):
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if info_dict := {key.replace(REC_TAC, ''): val for key, val in info.items() if key.startswith(f'{REC_TAC}')}:
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if self._entities:
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info_dict = {k: v for k, v in info_dict.items() if k in self._entities}
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info_dict.update(episode=(self.num_timesteps + env_idx))
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self._recorder_dict[env_idx].append(info_dict)
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else:
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pass
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return
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def _read_done(self, env_idx, done):
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if done:
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self._recorder_out_list.append({'steps': self._recorder_dict[env_idx],
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'episode': len(self._recorder_out_list)})
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self._recorder_dict[env_idx] = list()
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else:
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pass
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def save_records(self, filepath: Union[Path, str], save_occupation_map=False, save_trajectory_map=False):
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filepath = Path(filepath)
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filepath.parent.mkdir(exist_ok=True, parents=True)
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# self.out_file.unlink(missing_ok=True)
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with filepath.open('w') as f:
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out_dict = {'episodes': self._recorder_out_list, 'header': self.unwrapped.params}
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try:
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simplejson.dump(out_dict, f, indent=4)
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except TypeError:
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print('Shit')
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if save_occupation_map:
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a = np.zeros((15, 15))
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for episode in out_dict['episodes']:
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df = pd.DataFrame([y for x in episode['steps'] for y in x['Agents']])
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b = list(df[['x', 'y']].to_records(index=False))
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np.add.at(a, tuple(zip(*b)), 1)
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# a = np.rot90(a)
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import seaborn as sns
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from matplotlib import pyplot as plt
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hm = sns.heatmap(data=a)
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hm.set_title('Very Nice Heatmap')
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plt.show()
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if save_trajectory_map:
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raise NotImplementedError('This has not yet been implemented.')
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def _on_step(self) -> bool:
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for env_idx, info in enumerate(self.locals.get('infos', [])):
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self._read_info(env_idx, info)
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dones = list(enumerate(self.locals.get('dones', [])))
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dones.extend(list(enumerate(self.locals.get('done', []))))
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for env_idx, done in dones:
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self._read_done(env_idx, done)
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return True
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def _on_training_end(self) -> None:
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pass
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