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Adjustments and Documentation

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Steffen Illium 2022-05-03 11:32:19 +02:00
parent 33f144fc93
commit e7461d7dcf
2 changed files with 193 additions and 1 deletions
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2
environments/factory/factory_dirt.py
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@ -33,7 +33,7 @@ class RewardsDirt(NamedTuple):
class DirtProperties(NamedTuple):
initial_dirt_ratio: float = 0.3 # On INIT, on max how many tiles does the dirt spawn in percent.
initial_dirt_spawn_r_var: float = 0.05 # How much does the dirt spawn amount vary?
initial_dirt_spawn_r_var: float = 0.05 # How much does the dirt spawn amount vary?
clean_amount: float = 1 # How much does the robot clean with one actions.
max_spawn_ratio: float = 0.20 # On max how many tiles does the dirt spawn in percent.
max_spawn_amount: float = 0.3 # How much dirt does spawn per tile at max.

192
quickstart/single_agent_train_dirt_env.py Normal file
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@ -0,0 +1,192 @@
import sys
import time
from pathlib import Path
from matplotlib import pyplot as plt
import itertools as it
import stable_baselines3 as sb3
try:
# noinspection PyUnboundLocalVariable
if __package__ is None:
DIR = Path(__file__).resolve().parent
sys.path.insert(0, str(DIR.parent))
__package__ = DIR.name
else:
DIR = None
except NameError:
DIR = None
pass
import simplejson
from stable_baselines3.common.vec_env import SubprocVecEnv
from environments import helpers as h
from environments.factory.factory_dirt import DirtProperties, DirtFactory
from environments.logging.envmonitor import EnvMonitor
from environments.utility_classes import MovementProperties, ObservationProperties, AgentRenderOptions
import pickle
from plotting.compare_runs import compare_seed_runs, compare_model_runs
import pandas as pd
import seaborn as sns
import multiprocessing as mp
"""
Welcome to this quick start file. Here we will see how to:
0. Setup I/O Paths
1. Setup parameters for the environments (dirt-factory).
2. Setup parameters for the agent training (SB3: PPO) and save metrics.
Run the training.
3. Save env and agent for later analysis.
4. Load the agent from drive
5. Rendering the env with a run of the trained agent.
6. Plot metrics
"""
if __name__ == '__main__':
#########################################################
# 0. Setup I/O Paths
# Define some general parameters
train_steps = 1e6
n_seeds = 3
model_class = sb3.PPO
env_class = DirtFactory
# Define a global studi save path
start_time = int(time.time())
study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}_{start_time}'
# Create an identifier, which is unique for every combination and easy to read in filesystem
identifier = f'{model_class.__name__}_{env_class.__name__}_{start_time}'
exp_path = study_root_path / identifier
#########################################################
# 1. Setup parameters for the environments (dirt-factory).
# Define property object parameters.
# 'ObservationProperties' are for specifying how the agent sees the env.
obs_props = ObservationProperties(render_agents=AgentRenderOptions.NOT, # Agents won`t be shown in the obs at all
omit_agent_self=True, # This is default
additional_agent_placeholder=None, # We will not take care of future agents
frames_to_stack=3, # To give the agent a notion of time
pomdp_r=2 # the agents view-radius
)
# 'MovementProperties' are for specifying how the agent is allowed to move in the env.
move_props = MovementProperties(allow_diagonal_movement=True, # Euclidean style (vertices)
allow_square_movement=True, # Manhattan (edges)
allow_no_op=False) # Pause movement (do nothing)
# 'DirtProperties' control if and how dirt is spawned
# TODO: Comments
dirt_props = DirtProperties(initial_dirt_ratio=0.35,
initial_dirt_spawn_r_var=0.1,
clean_amount=0.34,
max_spawn_amount=0.1,
max_global_amount=20,
max_local_amount=1,
spawn_frequency=0,
max_spawn_ratio=0.05,
dirt_smear_amount=0.0)
# These are the EnvKwargs for initializing the env class, holding all former parameter-classes
# TODO: Comments
factory_kwargs = dict(n_agents=1,
max_steps=400,
parse_doors=True,
level_name='rooms',
doors_have_area=True, #
verbose=False,
mv_prop=move_props, # See Above
obs_prop=obs_props, # See Above
done_at_collision=True,
dirt_props=dirt_props
)
#########################################################
# 2. Setup parameters for the agent training (SB3: PPO) and save metrics.
agent_kwargs = dict()
#########################################################
# Run the Training
for seed in range(n_seeds):
# Make a copy if you want to alter things in the training loop; like the seed.
env_kwargs = factory_kwargs.copy()
env_kwargs.update(env_seed=seed)
# Output folder
seed_path = exp_path / f'{str(seed)}_{identifier}'
seed_path.mkdir(parents=True, exist_ok=True)
# Parameter Storage
param_path = seed_path / f'env_params.json'
# Observation (measures) Storage
monitor_path = seed_path / 'monitor.pick'
# Model save Path for the trained model
model_save_path = seed_path / f'model.zip'
# Env Init & Model kwargs definition
with DirtFactory(env_kwargs) as env_factory:
# EnvMonitor Init
env_monitor_callback = EnvMonitor(env_factory)
# Model Init
model = model_class("MlpPolicy", env_factory,verbose=1, seed=seed, device='cpu')
# Model train
model.learn(total_timesteps=int(train_steps), callback=[env_monitor_callback])
#########################################################
# 3. Save env and agent for later analysis.
# Save the trained Model, the monitor (env measures) and the env parameters
model.save(model_save_path)
env_factory.save_params(param_path)
env_monitor_callback.save_run(monitor_path)
# Compare performance runs, for each seed within a model
try:
compare_seed_runs(exp_path, use_tex=False)
except ValueError:
pass
# Train ends here ############################################################
# Evaluation starts here #####################################################
# First Iterate over every model and monitor "as trained"
print('Start Measurement Tracking')
# For trained policy in study_root_path / identifier
for policy_path in [x for x in exp_path.iterdir() if x.is_dir()]:
# retrieve model class
model_cls = next(val for key, val in h.MODEL_MAP.items() if key in policy_path.parent.name)
# Load the agent agent
model = model_cls.load(policy_path / 'model.zip', device='cpu')
# Load old env kwargs
with next(policy_path.glob('*.json')).open('r') as f:
env_kwargs = simplejson.load(f)
# Make the env stop ar collisions
# (you only want to have a single collision per episode hence the statistics)
env_kwargs.update(done_at_collision=True)
# Init Env
with env_to_run(**env_kwargs) as env_factory:
monitored_env_factory = EnvMonitor(env_factory)
# Evaluation Loop for i in range(n Episodes)
for episode in range(100):
env_state = monitored_env_factory.reset()
rew, done_bool = 0, False
while not done_bool:
action = model.predict(env_state, deterministic=True)[0]
env_state, step_r, done_bool, info_obj = monitored_env_factory.step(action)
rew += step_r
if done_bool:
break
print(f'Factory run {episode} done, reward is:\n {rew}')
monitored_env_factory.save_run(filepath=policy_path / f'{baseline_monitor_file}.pick')
# for policy_path in (y for y in policy_path.iterdir() if y.is_dir()):
# load_model_run_baseline(policy_path)
print('Measurements Done')