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new dirt paradigm -> clean everything up

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Steffen Illium
2021-10-27 18:47:57 +02:00
parent 35eae72a8d
commit b5c6105b7b
9 changed files with 210 additions and 114 deletions
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47
environments/factory/base/base_factory.py
View File

@@ -1,5 +1,6 @@
import abc
import time
from collections import defaultdict
from enum import Enum
from pathlib import Path
from typing import List, Union, Iterable, Dict
@@ -230,8 +231,9 @@ class BaseFactory(gym.Env):
del this_collisions[i]
guest.temp_collisions = this_collisions
if self.done_at_collision and tiles_with_collisions:
done = True
done = self.done_at_collision and tiles_with_collisions
done = done or self.check_additional_done()
# Step the door close intervall
if self.parse_doors:
@@ -440,48 +442,61 @@ class BaseFactory(gym.Env):
def calculate_reward(self) -> (int, dict):
# Returns: Reward, Info
info_dict = dict()
per_agent_info_dict = defaultdict(dict)
reward = 0
for agent in self[c.AGENT]:
if self._actions.is_moving_action(agent.temp_action):
if agent.temp_valid:
# info_dict.update(movement=1)
# info_dict.update({f'{agent.name}_failed_action': 1})
# reward += 0.00
pass
else:
# self.print('collision')
reward -= 0.01
self.print(f'{agent.name} just hit the wall at {agent.pos}.')
info_dict.update({f'{agent.name}_vs_LEVEL': 1})
per_agent_info_dict[agent.name].update({f'{agent.name}_vs_LEVEL': 1})
elif h.EnvActions.USE_DOOR == agent.temp_action:
if agent.temp_valid:
# reward += 0.00
self.print(f'{agent.name} did just use the door at {agent.pos}.')
info_dict.update(door_used=1)
per_agent_info_dict[agent.name].update(door_used=1)
else:
# reward -= 0.00
self.print(f'{agent.name} just tried to use a door at {agent.pos}, but failed.')
info_dict.update({f'{agent.name}_failed_action': 1})
info_dict.update({f'{agent.name}_failed_door_open': 1})
per_agent_info_dict[agent.name].update({f'{agent.name}_failed_door_open': 1})
elif h.EnvActions.NOOP == agent.temp_action:
info_dict.update(no_op=1)
per_agent_info_dict[agent.name].update(no_op=1)
# reward -= 0.00
# Monitor Notes
if agent.temp_valid:
per_agent_info_dict[agent.name].update(valid_action=1)
per_agent_info_dict[agent.name].update({f'{agent.name}_valid_action': 1})
else:
per_agent_info_dict[agent.name].update(failed_action=1)
per_agent_info_dict[agent.name].update({f'{agent.name}_failed_action': 1})
additional_reward, additional_info_dict = self.calculate_additional_reward(agent)
reward += additional_reward
info_dict.update(additional_info_dict)
per_agent_info_dict[agent.name].update(additional_info_dict)
if agent.temp_collisions:
self.print(f't = {self._steps}\t{agent.name} has collisions with {agent.temp_collisions}')
per_agent_info_dict[agent.name].update(collisions=1)
for other_agent in agent.temp_collisions:
info_dict.update({f'{agent.name}_vs_{other_agent.name}': 1})
for other_agent in agent.temp_collisions:
per_agent_info_dict[agent.name].update({f'{agent.name}_vs_{other_agent.name}': 1})
# Combine the per_agent_info_dict:
combined_info_dict = defaultdict(lambda: 0)
for info_dict in per_agent_info_dict.values():
for key, value in info_dict.items():
combined_info_dict[key] += value
combined_info_dict = dict(combined_info_dict)
self.print(f"reward is {reward}")
return reward, info_dict
return reward, combined_info_dict
def render(self, mode='human'):
if not self._renderer: # lazy init
@@ -565,6 +580,10 @@ class BaseFactory(gym.Env):
def do_additional_actions(self, agent: Agent, action: Action) -> Union[None, c]:
return None
@abc.abstractmethod
def check_additional_done(self) -> bool:
return False
@abc.abstractmethod
def calculate_additional_reward(self, agent: Agent) -> (int, dict):
return 0, {}

59
environments/factory/factory_dirt.py
View File

@@ -20,14 +20,17 @@ CLEAN_UP_ACTION = h.EnvActions.CLEAN_UP
class DirtProperties(NamedTuple):
clean_amount: int = 1 # How much does the robot clean with one actions.
max_spawn_ratio: float = 0.2 # On max how much tiles does the dirt spawn in percent.
gain_amount: float = 0.3 # How much dirt does spawn per tile.
spawn_frequency: int = 5 # Spawn Frequency in Steps.
max_local_amount: int = 2 # Max dirt amount per tile.
max_global_amount: int = 20 # Max dirt amount in the whole environment.
dirt_smear_amount: float = 0.2 # Agents smear dirt, when not cleaning up in place.
agent_can_interact: bool = True # Whether the agents can interact with the dirt in this environment.
initial_dirt_ratio: float = 0.3 # On INIT, on max how much tiles does the dirt spawn in percent.
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 much tiles does the dirt spawn in percent.
max_spawn_amount: float = 0.3 # How much dirt does spawn per tile at max.
spawn_frequency: int = 0 # Spawn Frequency in Steps.
max_local_amount: int = 2 # Max dirt amount per tile.
max_global_amount: int = 20 # Max dirt amount in the whole environment.
dirt_smear_amount: float = 0.2 # Agents smear dirt, when not cleaning up in place.
agent_can_interact: bool = True # Whether the agents can interact with the dirt in this environment.
done_when_clean = True
class Dirt(Entity):
@@ -91,10 +94,10 @@ class DirtRegister(MovingEntityObjectRegister):
if not self.amount > self.dirt_properties.max_global_amount:
dirt = self.by_pos(tile.pos)
if dirt is None:
dirt = Dirt(tile, amount=self.dirt_properties.gain_amount)
dirt = Dirt(tile, amount=self.dirt_properties.max_spawn_amount)
self.register_item(dirt)
else:
new_value = dirt.amount + self.dirt_properties.gain_amount
new_value = dirt.amount + self.dirt_properties.max_spawn_amount
dirt.set_new_amount(min(new_value, self.dirt_properties.max_local_amount))
else:
return c.NOT_VALID
@@ -160,12 +163,17 @@ class DirtFactory(BaseFactory):
else:
return c.NOT_VALID
def trigger_dirt_spawn(self):
def trigger_dirt_spawn(self, initial_spawn=False):
dirt_rng = self._dirt_rng
free_for_dirt = [x for x in self[c.FLOOR]
if len(x.guests) == 0 or (len(x.guests) == 1 and isinstance(next(y for y in x.guests), Dirt))
]
self._dirt_rng.shuffle(free_for_dirt)
new_spawn = self._dirt_rng.uniform(0, self.dirt_properties.max_spawn_ratio)
if initial_spawn:
var = self.dirt_properties.initial_dirt_spawn_r_var
new_spawn = self.dirt_properties.initial_dirt_ratio + dirt_rng.uniform(-var, var)
else:
new_spawn = dirt_rng.uniform(0, self.dirt_properties.max_spawn_ratio)
n_dirt_tiles = max(0, int(new_spawn * len(free_for_dirt)))
self[c.DIRT].spawn_dirt(free_for_dirt[:n_dirt_tiles])
@@ -184,8 +192,9 @@ class DirtFactory(BaseFactory):
if self[c.DIRT].spawn_dirt(agent.tile):
new_pos_dirt = self[c.DIRT].by_pos(agent.pos)
new_pos_dirt.set_new_amount(max(0, new_pos_dirt.amount + smeared_dirt))
if not self._next_dirt_spawn:
if self._next_dirt_spawn < 0:
pass # No Dirt Spawn
elif not self._next_dirt_spawn:
self.trigger_dirt_spawn()
self._next_dirt_spawn = self.dirt_properties.spawn_frequency
else:
@@ -208,8 +217,13 @@ class DirtFactory(BaseFactory):
def do_additional_reset(self) -> None:
super().do_additional_reset()
self.trigger_dirt_spawn()
self._next_dirt_spawn = self.dirt_properties.spawn_frequency
self.trigger_dirt_spawn(initial_spawn=True)
self._next_dirt_spawn = self.dirt_properties.spawn_frequency if self.dirt_properties.spawn_frequency else -1
def check_additional_done(self):
super_done = super().check_additional_done()
done = self.dirt_properties.done_when_clean and (len(self[c.DIRT]) == 0)
return super_done or done
def calculate_additional_reward(self, agent: Agent) -> (int, dict):
reward, info_dict = super().calculate_additional_reward(agent)
@@ -233,9 +247,8 @@ class DirtFactory(BaseFactory):
else:
reward -= 0.01
self.print(f'{agent.name} just tried to clean up some dirt at {agent.pos}, but failed.')
info_dict.update({f'{agent.name}_failed_action': 1})
info_dict.update({f'{agent.name}_failed_action': 1})
info_dict.update({f'{agent.name}_failed_dirt_cleanup': 1})
info_dict.update(failed_dirt_clean=1)
# Potential based rewards ->
# track the last reward , minus the current reward = potential
@@ -243,12 +256,12 @@ class DirtFactory(BaseFactory):
if __name__ == '__main__':
render = False
render = True
dirt_props = DirtProperties(1, 0.05, 0.1, 3, 1, 20, 0.0)
move_props = {'allow_square_movement': True,
'allow_diagonal_movement': False,
'allow_no_op': False} #MovementProperties(True, True, False)
'allow_diagonal_movement': False,
'allow_no_op': False} #MovementProperties(True, True, False)
with RecorderCallback(filepath=Path('debug_out') / f'recorder_xxxx.json', occupation_map=False,
trajectory_map=False) as recorder:
@@ -272,12 +285,12 @@ if __name__ == '__main__':
r = 0
for agent_i_action in random_actions:
env_state, step_r, done_bool, info_obj = factory.step(agent_i_action)
recorder.read_info(0, info_obj)
#recorder.read_info(0, info_obj)
r += step_r
if render:
factory.render()
if done_bool:
recorder.read_done(0, done_bool)
# recorder.read_done(0, done_bool)
break
print(f'Factory run {epoch} done, reward is:\n {r}')
pass

6
environments/factory/factory_dirt_item.py
View File

@@ -25,7 +25,7 @@ if __name__ == '__main__':
allow_square_movement=True,
allow_no_op=False)
render = False
render = True
factory = DirtItemFactory(n_agents=1, done_at_collision=False, frames_to_stack=0,
level_name='rooms', max_steps=200, combin_agent_obs=True,
@@ -46,12 +46,12 @@ if __name__ == '__main__':
r = 0
for agent_i_action in random_actions:
env_state, step_r, done_bool, info_obj = factory.step(agent_i_action)
recorder.read_info(0, info_obj)
# recorder.read_info(0, info_obj)
r += step_r
if render:
factory.render()
if done_bool:
recorder.read_done(0, done_bool)
# recorder.read_done(0, done_bool)
break
print(f'Factory run {epoch} done, reward is:\n {r}')
pass

19
environments/factory/factory_item.py
View File

@@ -318,17 +318,26 @@ class ItemFactory(BaseFactory):
if h.EnvActions.ITEM_ACTION == agent.temp_action:
if agent.temp_valid:
if drop_off := self[c.DROP_OFF].by_pos(agent.pos):
info_dict.update({f'{agent.name}_item_dropoff': 1})
info_dict.update({f'{agent.name}_item_drop_off': 1})
info_dict.update(item_drop_off=1)
self.print(f'{agent.name} just dropped of an item at {drop_off.pos}.')
reward += 0.5
else:
info_dict.update({f'{agent.name}_item_pickup': 1})
info_dict.update(item_pickup=1)
self.print(f'{agent.name} just picked up an item at {agent.pos}')
reward += 0.1
else:
info_dict.update({f'{agent.name}_failed_item_action': 1})
self.print(f'{agent.name} just tried to pick up an item at {agent.pos}, but failed.')
reward -= 0.1
if self[c.DROP_OFF].by_pos(agent.pos):
info_dict.update({f'{agent.name}_failed_drop_off': 1})
info_dict.update(failed_drop_off=1)
self.print(f'{agent.name} just tried to drop off at {agent.pos}, but failed.')
reward -= 0.1
else:
info_dict.update({f'{agent.name}_failed_item_action': 1})
info_dict.update(failed_pick_up=1)
self.print(f'{agent.name} just tried to pick up an item at {agent.pos}, but failed.')
reward -= 0.1
return reward, info_dict
def render_additional_assets(self, mode='human'):
@@ -343,7 +352,7 @@ class ItemFactory(BaseFactory):
if __name__ == '__main__':
import random
render = False
render = True
item_props = ItemProperties()

3
environments/logging/recorder.py
View File

@@ -33,7 +33,8 @@ class RecorderCallback(BaseCallback):
def read_done(self, env_idx, done):
if done:
self._recorder_out_list.append({'steps': self._recorder_dict[env_idx]})
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

15
plotting/compare_runs.py
View File

@@ -10,7 +10,7 @@ from environments.helpers import IGNORED_DF_COLUMNS, MODEL_MAP
from plotting.plotting import prepare_plot
def compare_seed_runs(run_path: Union[str, PathLike]):
def compare_seed_runs(run_path: Union[str, PathLike], use_tex: bool = False):
run_path = Path(run_path)
df_list = list()
for run, monitor_file in enumerate(run_path.rglob('monitor*.pick')):
@@ -37,11 +37,12 @@ def compare_seed_runs(run_path: Union[str, PathLike]):
skip_n = round(df_melted['Episode'].max() * 0.02)
df_melted = df_melted[df_melted['Episode'] % skip_n == 0]
prepare_plot(run_path / f'{run_path.name}_monitor_lineplot.png', df_melted)
prepare_plot(run_path / f'{run_path.name}_monitor_lineplot.png', df_melted, use_tex=use_tex)
print('Plotting done.')
def compare_model_runs(run_path: Path, run_identifier: Union[str, int], parameter: Union[str, List[str]]):
def compare_model_runs(run_path: Path, run_identifier: Union[str, int], parameter: Union[str, List[str]],
use_tex: bool = False):
run_path = Path(run_path)
df_list = list()
parameter = [parameter] if isinstance(parameter, str) else parameter
@@ -75,12 +76,13 @@ def compare_model_runs(run_path: Path, run_identifier: Union[str, int], paramete
df_melted = df_melted[df_melted['Episode'] % skip_n == 0]
style = 'Measurement' if len(columns) > 1 else None
prepare_plot(run_path / f'{run_identifier}_compare_{parameter}.png', df_melted, hue='Model', style=style)
prepare_plot(run_path / f'{run_identifier}_compare_{parameter}.png', df_melted, hue='Model', style=style,
use_tex=use_tex)
print('Plotting done.')
def compare_all_parameter_runs(run_root_path: Path, parameter: Union[str, List[str]],
param_names: Union[List[str], None] = None, str_to_ignore=''):
param_names: Union[List[str], None] = None, str_to_ignore='', use_tex: bool = False):
run_root_path = Path(run_root_path)
df_list = list()
parameter = [parameter] if isinstance(parameter, str) else parameter
@@ -151,5 +153,6 @@ def compare_all_parameter_runs(run_root_path: Path, parameter: Union[str, List[s
value_name="Score")
style = 'Measurement' if len(columns) > 1 else None
prepare_plot(run_root_path / f'compare_{parameter}.png', df_melted, hue='Parameter Combination', style=style)
prepare_plot(run_root_path / f'compare_{parameter}.png', df_melted, hue='Parameter Combination',
style=style, use_tex=use_tex)
print('Plotting done.')

42
plotting/plotting.py
View File

@@ -26,21 +26,35 @@ def plot(filepath, ext='png'):
plt.clf()
def prepare_plot(filepath, results_df, ext='png', hue='Measurement', style=None):
def prepare_tex(df, hue, style, hue_order):
sns.set(rc={'text.usetex': True}, style='whitegrid')
lineplot = sns.lineplot(data=df, x='Episode', y='Score', ci=95, palette=PALETTE,
hue_order=hue_order, hue=hue, style=style)
# lineplot.set_title(f'{sorted(list(df["Measurement"].unique()))}')
return lineplot
def prepare_plt(df, hue, style, hue_order):
print('Struggling to plot Figure using LaTeX - going back to normal.')
plt.close('all')
sns.set(rc={'text.usetex': False}, style='whitegrid')
lineplot = sns.lineplot(data=df, x='Episode', y='Score', hue=hue, style=style,
ci=95, palette=PALETTE, hue_order=hue_order)
# lineplot.set_title(f'{sorted(list(df["Measurement"].unique()))}')
return lineplot
def prepare_plot(filepath, results_df, ext='png', hue='Measurement', style=None, use_tex=False):
df = results_df.copy()
df[hue] = df[hue].str.replace('_', '-')
hue_order = sorted(list(df[hue].unique()))
try:
sns.set(rc={'text.usetex': True}, style='whitegrid')
lineplot = sns.lineplot(data=df, x='Episode', y='Score', ci=95, palette=PALETTE,
hue_order=hue_order, hue=hue, style=style)
# lineplot.set_title(f'{sorted(list(df["Measurement"].unique()))}')
plot(filepath, ext=ext) # plot raises errors not lineplot!
except (FileNotFoundError, RuntimeError):
print('Struggling to plot Figure using LaTeX - going back to normal.')
plt.close('all')
sns.set(rc={'text.usetex': False}, style='whitegrid')
lineplot = sns.lineplot(data=df, x='Episode', y='Score', hue=hue, style=style,
ci=95, palette=PALETTE, hue_order=hue_order)
# lineplot.set_title(f'{sorted(list(df["Measurement"].unique()))}')
if use_tex:
try:
_ = prepare_tex(df, hue, style, hue_order)
plot(filepath, ext=ext) # plot raises errors not lineplot!
except (FileNotFoundError, RuntimeError):
_ = prepare_plt(df, hue, style, hue_order)
plot(filepath, ext=ext)
else:
_ = prepare_plt(df, hue, style, hue_order)
plot(filepath, ext=ext)

31
reload_agent.py
View File

@@ -1,6 +1,7 @@
import warnings
from pathlib import Path
import numpy as np
import yaml
from environments import helpers as h
@@ -14,36 +15,42 @@ warnings.filterwarnings('ignore', category=UserWarning)
if __name__ == '__main__':
model_name = 'PPO_1631187073'
model_name = 'DQN_1631187073'
run_id = 0
seed = 69
out_path = Path(__file__).parent / 'study_out' / 'e_1_1631709932' / 'no_obs' / 'dirt' / 'A2C_1631709932' / '0_A2C_1631709932'
model_path = out_path / model_name
out_path = Path('debug_out/DQN_1635176929/0_DQN_1635176929')
model_path = out_path
with (out_path / f'env_params.json').open('r') as f:
env_kwargs = yaml.load(f, Loader=yaml.FullLoader)
env_kwargs.update(additional_agent_placeholder=None)
# env_kwargs.update(verbose=False, env_seed=seed, record_episodes=True, parse_doors=True)
env_kwargs.update(additional_agent_placeholder=None, n_agents=4)
if gain_amount := env_kwargs.get('dirt_properties', {}).get('gain_amount', None):
env_kwargs['dirt_properties']['max_spawn_amount'] = gain_amount
del env_kwargs['dirt_properties']['gain_amount']
env_kwargs.update(record_episodes=True)
this_model = out_path / 'model.zip'
model_cls = next(val for key, val in h.MODEL_MAP.items() if key in model_name)
model = model_cls.load(this_model)
models = [model_cls.load(this_model) for _ in range(4)]
with RecorderCallback(filepath=Path() / 'recorder_out_doors.json') as recorder:
with RecorderCallback(filepath=Path() / 'recorder_out_DQN.json') as recorder:
# Init Env
with DirtFactory(**env_kwargs) as env:
with DirtItemFactory(**env_kwargs) as env:
obs_shape = env.observation_space.shape
# Evaluation Loop for i in range(n Episodes)
for episode in range(5):
obs = env.reset()
env_state = env.reset()
rew, done_bool = 0, False
while not done_bool:
action = model.predict(obs, deterministic=False)[0]
env_state, step_r, done_bool, info_obj = env.step(action[0])
actions = [model.predict(
np.stack([env_state[i][j] for i in range(env_state.shape[0])]),
deterministic=True)[0] for j, model in enumerate(models)]
env_state, step_r, done_bool, info_obj = env.step(actions)
recorder.read_info(0, info_obj)
rew += step_r
env.render()
# env.render()
if done_bool:
recorder.read_done(0, done_bool)
break

102
studies/e_1.py
View File

@@ -33,7 +33,7 @@ import pandas as pd
import seaborn as sns
# Define a global studi save path
start_time = 1634134997 # int(time.time())
start_time = 163519000 # int(time.time())
study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}_{start_time}'
"""
@@ -70,7 +70,7 @@ There are further distinctions to be made:
def policy_model_kwargs():
return dict(ent_coef=0.01)
return dict(ent_coef=0.05)
def dqn_model_kwargs():
@@ -93,21 +93,23 @@ def encapsule_env_factory(env_fctry, env_kwrgs):
if __name__ == '__main__':
train_steps = 5e5
train_steps = 8e5
# Define Global Env Parameters
# Define properties object parameters
move_props = MovementProperties(allow_diagonal_movement=True,
allow_square_movement=True,
allow_no_op=False)
dirt_props = DirtProperties(clean_amount=2, gain_amount=0.1, max_global_amount=20,
max_local_amount=1, spawn_frequency=15, max_spawn_ratio=0.05,
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, agent_can_interact=True)
item_props = ItemProperties(n_items=10, agent_can_interact=True,
spawn_frequency=30, n_drop_off_locations=2,
max_agent_inventory_capacity=15)
factory_kwargs = dict(n_agents=1,
pomdp_r=2, max_steps=400, parse_doors=False,
pomdp_r=2, max_steps=400, parse_doors=True,
level_name='rooms', frames_to_stack=3,
omit_agent_in_obs=True, combin_agent_obs=True, record_episodes=False,
cast_shadows=True, doors_have_area=False, verbose=False,
@@ -124,9 +126,9 @@ if __name__ == '__main__':
# Define parameter versions according with #1,2[1,0,N],3
observation_modes = {
# Fill-value = 0
'seperate_0': dict(additional_env_kwargs=dict(additional_agent_placeholder=0)),
# DEACTIVATED 'seperate_0': dict(additional_env_kwargs=dict(additional_agent_placeholder=0)),
# Fill-value = 1
'seperate_1': dict(additional_env_kwargs=dict(additional_agent_placeholder=1)),
# DEACTIVATED 'seperate_1': dict(additional_env_kwargs=dict(additional_agent_placeholder=1)),
# Fill-value = N(0, 1)
'seperate_N': dict(additional_env_kwargs=dict(additional_agent_placeholder='N')),
# Further Adjustments are done post-training
@@ -137,10 +139,10 @@ if __name__ == '__main__':
# Train starts here ############################################################
# Build Major Loop parameters, parameter versions, Env Classes and models
if False:
if True:
for observation_mode in observation_modes.keys():
for env_name in env_names:
for model_cls in h.MODEL_MAP.values():
for model_cls in [h.MODEL_MAP['A2C'], h.MODEL_MAP['DQN']]:
# Create an identifier, which is unique for every combination and easy to read in filesystem
identifier = f'{model_cls.__name__}_{start_time}'
# Train each combination per seed
@@ -154,6 +156,8 @@ if __name__ == '__main__':
env_kwargs.update(env_seed=seed)
# Output folder
seed_path = combination_path / f'{str(seed)}_{identifier}'
if (seed_path / 'monitor.pick').exists():
continue
seed_path.mkdir(parents=True, exist_ok=True)
# Monitor Init
@@ -163,7 +167,7 @@ if __name__ == '__main__':
if model_cls.__name__ in ["PPO", "A2C"]:
# env_factory = env_class(**env_kwargs)
env_factory = SubprocVecEnv([encapsule_env_factory(env_class, env_kwargs)
for _ in range(1)], start_method="spawn")
for _ in range(6)], start_method="spawn")
model_kwargs = policy_model_kwargs()
elif model_cls.__name__ in ["RegDQN", "DQN", "QRDQN"]:
@@ -197,15 +201,20 @@ if __name__ == '__main__':
gc.collect()
# Compare performance runs, for each seed within a model
compare_seed_runs(combination_path)
compare_seed_runs(combination_path, use_tex=False)
# Better be save then sorry: Clean up!
del model_kwargs, env_kwargs
import gc
gc.collect()
try:
del env_kwargs
del model_kwargs
import gc
gc.collect()
except NameError:
pass
# Compare performance runs, for each model
# FIXME: Check THIS!!!!
compare_model_runs(study_root_path / observation_mode / env_name, f'{start_time}', 'step_reward')
compare_model_runs(study_root_path / observation_mode / env_name, f'{start_time}', 'step_reward',
use_tex=False)
pass
pass
pass
@@ -215,7 +224,7 @@ if __name__ == '__main__':
# Evaluation starts here #####################################################
# First Iterate over every model and monitor "as trained"
baseline_monitor_file = 'e_1_baseline_monitor.pick'
if False:
if True:
render = False
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)
@@ -312,8 +321,9 @@ if __name__ == '__main__':
# Plotting
if True:
# TODO: Plotting
df_list = list()
for observation_folder in (x for x in study_root_path.iterdir() if x.is_dir()):
df_list = list()
for env_folder in (x for x in observation_folder.iterdir() if x.is_dir()):
for model_folder in (x for x in env_folder.iterdir() if x.is_dir()):
# Gather per seed results in this list
@@ -334,28 +344,48 @@ if __name__ == '__main__':
monitor_df['obs_mode'] = monitor_df['obs_mode'].astype(str)
monitor_df['model'] = model_folder.name.split('_')[0]
df_list.append(monitor_df)
id_cols = ['monitor', 'env', 'obs_mode', 'model']
id_cols = ['monitor', 'env', 'obs_mode', 'model']
df = pd.concat(df_list, ignore_index=True)
df = df.fillna(0)
df = pd.concat(df_list, ignore_index=True)
df = df.fillna(0)
for id_col in id_cols:
df[id_col] = df[id_col].astype(str)
for id_col in id_cols:
df[id_col] = df[id_col].astype(str)
df_grouped = df.groupby(id_cols + ['seed']
).agg({key: 'sum' if "Agent" in key else 'mean' for key in df.columns
if key not in (id_cols + ['seed'])})
df_melted = df_grouped.reset_index().melt(id_vars=id_cols,
value_vars='step_reward', var_name="Measurement",
value_name="Score")
if True:
# df['fail_sum'] = df.loc[:, df.columns.str.contains("failed")].sum(1)
df['pick_up'] = df.loc[:, df.columns.str.contains("]_item_pickup")].sum(1)
df['drop_off'] = df.loc[:, df.columns.str.contains("]_item_dropoff")].sum(1)
df['failed_item_action'] = df.loc[:, df.columns.str.contains("]_failed_item_action")].sum(1)
df['failed_cleanup'] = df.loc[:, df.columns.str.contains("]_failed_dirt_cleanup")].sum(1)
df['coll_lvl'] = df.loc[:, df.columns.str.contains("]_vs_LEVEL")].sum(1)
df['coll_agent'] = df.loc[:, df.columns.str.contains("]_vs_Agent")].sum(1) / 2
# df['collisions'] = df['coll_lvl'] + df['coll_agent']
c = sns.catplot(data=df_melted, x='obs_mode', hue='monitor', row='model', col='env', y='Score', sharey=False,
kind="box", height=4, aspect=.7, legend_out=True)
c.set_xticklabels(rotation=65, horizontalalignment='right')
plt.tight_layout(pad=2)
plt.savefig(study_root_path / f'results_{n_agents}_agents.png')
value_vars = ['pick_up', 'drop_off', 'failed_item_action', 'failed_cleanup',
'coll_lvl', 'coll_agent', 'dirt_cleaned']
pass
df_grouped = df.groupby(id_cols + ['seed']
).agg({key: 'sum' if "Agent" in key else 'mean' for key in df.columns
if key not in (id_cols + ['seed'])})
df_melted = df_grouped.reset_index().melt(id_vars=id_cols,
value_vars=value_vars, # 'step_reward',
var_name="Measurement",
value_name="Score")
# df_melted["Measurements"] = df_melted["Measurement"] + " " + df_melted["monitor"]
# Plotting
fig, ax = plt.subplots(figsize=(11.7, 8.27))
c = sns.catplot(data=df_melted[df_melted['obs_mode'] == observation_folder.name],
x='Measurement', hue='monitor', row='model', col='env', y='Score',
sharey=False, kind="box", height=4, aspect=.7, legend_out=True,
showfliers=False)
c.set_xticklabels(rotation=65, horizontalalignment='right')
c.fig.subplots_adjust(top=0.9) # adjust the Figure in rp
c.fig.suptitle(f"Cat plot for {observation_folder.name}")
plt.tight_layout(pad=2)
plt.savefig(study_root_path / f'results_{n_agents}_agents_{observation_folder.name}.png')
pass