mirror of
https://github.com/illiumst/marl-factory-grid.git
synced 2025-05-23 07:16:44 +02:00
103 lines
3.4 KiB
Python
103 lines
3.4 KiB
Python
from collections import defaultdict
|
|
from pathlib import Path
|
|
from typing import Union
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
import simplejson
|
|
from stable_baselines3.common.callbacks import BaseCallback
|
|
|
|
from environments.factory.base.base_factory import REC_TAC
|
|
|
|
|
|
class EnvRecorder(BaseCallback):
|
|
|
|
def __init__(self, env, entities='all'):
|
|
super(EnvRecorder, self).__init__()
|
|
self.unwrapped = env
|
|
self._recorder_dict = defaultdict(list)
|
|
self._recorder_out_list = list()
|
|
if isinstance(entities, str):
|
|
if entities.lower() == 'all':
|
|
self._entities = None
|
|
else:
|
|
self._entities = [entities]
|
|
else:
|
|
self._entities = entities
|
|
self.started = False
|
|
self.closed = False
|
|
|
|
def __getattr__(self, item):
|
|
return getattr(self.unwrapped, item)
|
|
|
|
def reset(self):
|
|
self.unwrapped._record_episodes = True
|
|
return self.unwrapped.reset()
|
|
|
|
def _on_training_start(self) -> None:
|
|
self.unwrapped._record_episodes = True
|
|
pass
|
|
|
|
def _read_info(self, env_idx, info: dict):
|
|
if info_dict := {key.replace(REC_TAC, ''): val for key, val in info.items() if key.startswith(f'{REC_TAC}')}:
|
|
if self._entities:
|
|
info_dict = {k: v for k, v in info_dict.items() if k in self._entities}
|
|
|
|
info_dict.update(episode=(self.num_timesteps + env_idx))
|
|
self._recorder_dict[env_idx].append(info_dict)
|
|
else:
|
|
pass
|
|
return
|
|
|
|
def _read_done(self, env_idx, done):
|
|
if done:
|
|
self._recorder_out_list.append({'steps': self._recorder_dict[env_idx],
|
|
'episode': len(self._recorder_out_list)})
|
|
self._recorder_dict[env_idx] = list()
|
|
else:
|
|
pass
|
|
|
|
def save_records(self, filepath: Union[Path, str], save_occupation_map=False, save_trajectory_map=False):
|
|
filepath = Path(filepath)
|
|
filepath.parent.mkdir(exist_ok=True, parents=True)
|
|
# cls.out_file.unlink(missing_ok=True)
|
|
with filepath.open('w') as f:
|
|
out_dict = {'episodes': self._recorder_out_list, 'header': self.unwrapped.params}
|
|
try:
|
|
simplejson.dump(out_dict, f, indent=4)
|
|
except TypeError:
|
|
print('Shit')
|
|
|
|
if save_occupation_map:
|
|
a = np.zeros((15, 15))
|
|
for episode in out_dict['episodes']:
|
|
df = pd.DataFrame([y for x in episode['steps'] for y in x['Agents']])
|
|
|
|
b = list(df[['x', 'y']].to_records(index=False))
|
|
|
|
np.add.at(a, tuple(zip(*b)), 1)
|
|
|
|
# a = np.rot90(a)
|
|
import seaborn as sns
|
|
from matplotlib import pyplot as plt
|
|
hm = sns.heatmap(data=a)
|
|
hm.set_title('Very Nice Heatmap')
|
|
plt.show()
|
|
|
|
if save_trajectory_map:
|
|
raise NotImplementedError('This has not yet been implemented.')
|
|
|
|
def _on_step(self) -> bool:
|
|
for env_idx, info in enumerate(self.locals.get('infos', [])):
|
|
self._read_info(env_idx, info)
|
|
|
|
dones = list(enumerate(self.locals.get('dones', [])))
|
|
dones.extend(list(enumerate(self.locals.get('done', []))))
|
|
for env_idx, done in dones:
|
|
self._read_done(env_idx, done)
|
|
|
|
return True
|
|
|
|
def _on_training_end(self) -> None:
|
|
pass
|