recoder adaption
This commit is contained in:
Steffen Illium
.gitignore__init__.pymain.pymain_test.py
algorithms
environments
factory
helpers.pylogging
plotting
reload_agent.pysetup.pystudies
108
main.py
108
main.py
@ -1,101 +1,27 @@
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import pickle
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import warnings
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from typing import Union, List
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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 stable_baselines3.common.vec_env import SubprocVecEnv
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from environments.factory.factory_dirt_item import DirtItemFactory
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from environments.factory.factory_item import ItemFactory, ItemProperties
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from environments.factory.factory_dirt import DirtProperties, DirtFactory
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from environments.helpers import IGNORED_DF_COLUMNS
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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.recorder import RecorderCallback
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from environments.utility_classes import MovementProperties
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from plotting.compare_runs import compare_seed_runs, compare_model_runs
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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_df = pickle.load(f)
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monitor_df['run'] = run
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monitor_df = monitor_df.fillna(0)
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df_list.append(monitor_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'}).sort_values(['Run', 'Episode'])
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columns = [col for col in df.columns if col not in IGNORED_DF_COLUMNS]
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roll_n = 50
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non_overlapp_window = df.groupby(['Run', 'Episode']).rolling(roll_n, min_periods=1).mean()
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df_melted = non_overlapp_window[columns].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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if df_melted['Episode'].max() > 800:
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skip_n = round(df_melted['Episode'].max() * 0.02)
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df_melted = df_melted[df_melted['Episode'] % skip_n == 0]
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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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def compare_runs(run_path: Path, run_identifier: int, parameter: Union[str, List[str]]):
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run_path = Path(run_path)
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df_list = list()
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parameter = [parameter] if isinstance(parameter, str) else parameter
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for path in run_path.iterdir():
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if path.is_dir() and str(run_identifier) in path.name:
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for run, monitor_file in enumerate(path.rglob('monitor_*.pick')):
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with monitor_file.open('rb') as f:
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monitor_df = pickle.load(f)
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monitor_df['run'] = run
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monitor_df['model'] = path.name.split('_')[0]
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monitor_df = monitor_df.fillna(0)
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df_list.append(monitor_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', 'model': 'Model'})
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columns = [col for col in df.columns if col in parameter]
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last_episode_to_report = min(df.groupby(['Model'])['Episode'].max())
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df = df[df['Episode'] < last_episode_to_report]
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roll_n = 40
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non_overlapp_window = df.groupby(['Model', 'Run', 'Episode']).rolling(roll_n, min_periods=1).mean()
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df_melted = non_overlapp_window[columns].reset_index().melt(id_vars=['Episode', 'Run', 'Model'],
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value_vars=columns, var_name="Measurement",
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value_name="Score")
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if df_melted['Episode'].max() > 100:
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skip_n = round(df_melted['Episode'].max() * 0.02)
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df_melted = df_melted[df_melted['Episode'] % skip_n == 0]
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style = 'Measurement' if len(columns) > 1 else None
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prepare_plot(run_path / f'{run_identifier}_compare_{parameter}.png', df_melted, hue='Model', style=style)
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print('Plotting done.')
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def make_env(env_kwargs_dict):
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def _init():
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with ItemFactory(**env_kwargs_dict) as init_env:
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with DirtFactory(**env_kwargs_dict) as init_env:
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return init_env
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return _init
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@ -110,17 +36,19 @@ if __name__ == '__main__':
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# exit()
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from stable_baselines3 import PPO, DQN, A2C
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from algorithms.reg_dqn import RegDQN
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# from algorithms.reg_dqn import RegDQN
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# from sb3_contrib import QRDQN
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dirt_props = DirtProperties(clean_amount=2, gain_amount=0.1, max_global_amount=20,
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max_local_amount=1, spawn_frequency=3, max_spawn_ratio=0.05,
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max_local_amount=1, spawn_frequency=16, max_spawn_ratio=0.05,
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dirt_smear_amount=0.0, agent_can_interact=True)
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item_props = ItemProperties(n_items=5, agent_can_interact=True)
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item_props = ItemProperties(n_items=10, agent_can_interact=True,
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spawn_frequency=30, n_drop_off_locations=2,
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max_agent_inventory_capacity=15)
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move_props = MovementProperties(allow_diagonal_movement=True,
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allow_square_movement=True,
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allow_no_op=False)
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train_steps = 8e5
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train_steps = 5e6
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time_stamp = int(time.time())
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out_path = None
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@ -128,18 +56,18 @@ if __name__ == '__main__':
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for modeL_type in [A2C, PPO, DQN]: # ,RegDQN, QRDQN]:
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for seed in range(3):
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env_kwargs = dict(n_agents=1,
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item_properties=item_props,
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# dirt_properties=dirt_props,
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# item_properties=item_props,
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dirt_properties=dirt_props,
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movement_properties=move_props,
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pomdp_r=2, max_steps=400, parse_doors=False,
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level_name='rooms', frames_to_stack=3,
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pomdp_r=2, max_steps=1000, parse_doors=False,
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level_name='rooms', frames_to_stack=4,
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omit_agent_in_obs=True, combin_agent_obs=True, record_episodes=False,
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cast_shadows=True, doors_have_area=False, env_seed=seed, verbose=False,
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)
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if modeL_type.__name__ in ["PPO", "A2C"]:
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kwargs = dict(ent_coef=0.01)
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env = SubprocVecEnv([make_env(env_kwargs) for _ in range(1)], start_method="spawn")
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env = SubprocVecEnv([make_env(env_kwargs) for _ in range(10)], start_method="spawn")
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elif modeL_type.__name__ in ["RegDQN", "DQN", "QRDQN"]:
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env = make_env(env_kwargs)()
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kwargs = dict(buffer_size=50000,
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@ -161,7 +89,7 @@ if __name__ == '__main__':
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out_path /= identifier
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callbacks = CallbackList(
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[MonitorCallback(filepath=out_path / f'monitor_{identifier}.pick', plotting=False),
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[MonitorCallback(filepath=out_path / f'monitor_{identifier}.pick'),
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RecorderCallback(filepath=out_path / f'recorder_{identifier}.json', occupation_map=False,
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trajectory_map=False
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)]
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@ -172,7 +100,7 @@ if __name__ == '__main__':
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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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param_path = out_path.parent / f'env_{model.__class__.__name__}_{time_stamp}.yaml'
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param_path = out_path.parent / f'env_{model.__class__.__name__}_{time_stamp}.json'
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try:
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env.env_method('save_params', param_path)
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except AttributeError:
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@ -181,7 +109,7 @@ if __name__ == '__main__':
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print("Model Group Done.. Plotting...")
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if out_path:
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combine_runs(out_path.parent)
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compare_seed_runs(out_path.parent)
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print("All Models Done... Evaluating")
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if out_path:
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compare_runs(Path('debug_out'), time_stamp, 'step_reward')
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compare_model_runs(Path('debug_out'), time_stamp, 'step_reward')
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