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n agent experiments

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Steffen Illium
2021-10-20 17:24:06 +02:00
parent db4dbc13ae
commit 35eae72a8d
1 changed files with 33 additions and 29 deletions
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studies/e_1.py
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@ -1,6 +1,7 @@
import sys import sys
from pathlib import Path from pathlib import Path
from matplotlib import pyplot as plt from matplotlib import pyplot as plt
import numpy as np
try: try:
# noinspection PyUnboundLocalVariable # noinspection PyUnboundLocalVariable
@ -32,7 +33,7 @@ import pandas as pd
import seaborn as sns import seaborn as sns
# Define a global studi save path # Define a global studi save path
start_time = int(time.time()) start_time = 1634134997 # int(time.time())
study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}_{start_time}' study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}_{start_time}'
""" """
@ -136,7 +137,7 @@ if __name__ == '__main__':
# Train starts here ############################################################ # Train starts here ############################################################
# Build Major Loop parameters, parameter versions, Env Classes and models # Build Major Loop parameters, parameter versions, Env Classes and models
if True: if False:
for observation_mode in observation_modes.keys(): for observation_mode in observation_modes.keys():
for env_name in env_names: for env_name in env_names:
for model_cls in h.MODEL_MAP.values(): for model_cls in h.MODEL_MAP.values():
@ -210,12 +211,12 @@ if __name__ == '__main__':
pass pass
pass pass
# Train ends here ############################################################ # Train ends here ############################################################
exit()
# Evaluation starts here ##################################################### # Evaluation starts here #####################################################
# First Iterate over every model and monitor "as trained" # First Iterate over every model and monitor "as trained"
baseline_monitor_file = 'e_1_baseline_monitor.pick' baseline_monitor_file = 'e_1_baseline_monitor.pick'
if True: if False:
render = True render = False
for observation_mode in observation_modes: for observation_mode in observation_modes:
obs_mode_path = next(x for x in study_root_path.iterdir() if x.is_dir() and x.name == observation_mode) obs_mode_path = next(x for x in study_root_path.iterdir() if x.is_dir() and x.name == observation_mode)
# For trained policy in study_root_path / identifier # For trained policy in study_root_path / identifier
@ -233,22 +234,22 @@ if __name__ == '__main__':
# Monitor Init # Monitor Init
with MonitorCallback(filepath=seed_path / baseline_monitor_file) as monitor: with MonitorCallback(filepath=seed_path / baseline_monitor_file) as monitor:
# Init Env # Init Env
env_factory = env_map[env_path.name][0](**env_kwargs) with env_map[env_path.name][0](**env_kwargs) as env_factory:
# Evaluation Loop for i in range(n Episodes) # Evaluation Loop for i in range(n Episodes)
for episode in range(100): for episode in range(100):
obs = env_factory.reset() env_state = env_factory.reset()
rew, done_bool = 0, False rew, done_bool = 0, False
while not done_bool: while not done_bool:
action = model.predict(obs, deterministic=True)[0] action = model.predict(env_state, deterministic=True)[0]
env_state, step_r, done_bool, info_obj = env_factory.step(action) env_state, step_r, done_bool, info_obj = env_factory.step(action)
monitor.read_info(0, info_obj) monitor.read_info(0, info_obj)
rew += step_r rew += step_r
if render: if render:
env_factory.render() env_factory.render()
if done_bool: if done_bool:
monitor.read_done(0, done_bool) monitor.read_done(0, done_bool)
break break
print(f'Factory run {episode} done, reward is:\n {rew}') print(f'Factory run {episode} done, reward is:\n {rew}')
# Eval monitor outputs are automatically stored by the monitor object # Eval monitor outputs are automatically stored by the monitor object
del model, env_kwargs, env_factory del model, env_kwargs, env_factory
import gc import gc
@ -256,7 +257,9 @@ if __name__ == '__main__':
gc.collect() gc.collect()
# Then iterate over every model and monitor "ood behavior" - "is it ood?" # Then iterate over every model and monitor "ood behavior" - "is it ood?"
ood_monitor_file = 'e_1_monitor.pick' n_agents = 4
ood_monitor_file = f'e_1_monitor_{n_agents}_agents.pick'
if True: if True:
for observation_mode in observation_modes: for observation_mode in observation_modes:
obs_mode_path = next(x for x in study_root_path.iterdir() if x.is_dir() and x.name == observation_mode) obs_mode_path = next(x for x in study_root_path.iterdir() if x.is_dir() and x.name == observation_mode)
@ -268,17 +271,17 @@ if __name__ == '__main__':
# seed_path = next((y for y in policy_path.iterdir() if y.is_dir())) # seed_path = next((y for y in policy_path.iterdir() if y.is_dir()))
# Iteration # Iteration
for seed_path in (y for y in policy_path.iterdir() if y.is_dir()): for seed_path in (y for y in policy_path.iterdir() if y.is_dir()):
if (seed_path / f'e_1_monitor.pick').exists(): if (seed_path / ood_monitor_file).exists():
continue continue
# retrieve model class # retrieve model class
for model_cls in (val for key, val in h.MODEL_MAP.items() if key in policy_path.name): for model_cls in (val for key, val in h.MODEL_MAP.items() if key in policy_path.name):
# Load both agents # Load both agents
models = [model_cls.load(seed_path / 'model.zip') for _ in range(2)] models = [model_cls.load(seed_path / 'model.zip') for _ in range(n_agents)]
# Load old env kwargs # Load old env kwargs
with next(seed_path.glob('*.json')).open('r') as f: with next(seed_path.glob('*.json')).open('r') as f:
env_kwargs = simplejson.load(f) env_kwargs = simplejson.load(f)
env_kwargs.update( env_kwargs.update(
n_agents=2, additional_agent_placeholder=None, n_agents=n_agents, additional_agent_placeholder=None,
**observation_modes[observation_mode].get('post_training_env_kwargs', {})) **observation_modes[observation_mode].get('post_training_env_kwargs', {}))
# Monitor Init # Monitor Init
@ -287,11 +290,12 @@ if __name__ == '__main__':
with env_map[env_path.name][0](**env_kwargs) as env_factory: with env_map[env_path.name][0](**env_kwargs) as env_factory:
# Evaluation Loop for i in range(n Episodes) # Evaluation Loop for i in range(n Episodes)
for episode in range(50): for episode in range(50):
obs = env_factory.reset() env_state = env_factory.reset()
rew, done_bool = 0, False rew, done_bool = 0, False
while not done_bool: while not done_bool:
actions = [model.predict(obs[i], deterministic=False)[0] actions = [model.predict(
for i, model in enumerate(models)] np.stack([env_state[i][j] for i in range(env_state.shape[0])]),
deterministic=False)[0] for j, model in enumerate(models)]
env_state, step_r, done_bool, info_obj = env_factory.step(actions) env_state, step_r, done_bool, info_obj = env_factory.step(actions)
monitor.read_info(0, info_obj) monitor.read_info(0, info_obj)
rew += step_r rew += step_r
@ -352,6 +356,6 @@ if __name__ == '__main__':
kind="box", height=4, aspect=.7, legend_out=True) kind="box", height=4, aspect=.7, legend_out=True)
c.set_xticklabels(rotation=65, horizontalalignment='right') c.set_xticklabels(rotation=65, horizontalalignment='right')
plt.tight_layout(pad=2) plt.tight_layout(pad=2)
plt.show() plt.savefig(study_root_path / f'results_{n_agents}_agents.png')
pass pass