Debugging
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Steffen Illium
parent
3150757347
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b6c8cbd2e3
@ -5,11 +5,25 @@ from networkx.algorithms.approximation import traveling_salesman as tsp
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from environments.factory.base.objects import Agent
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from environments.helpers import points_to_graph
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from environments import helpers as h
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from environments.helpers import Constants as c
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from environments.helpers import Constants as BaseConstants
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from environments.helpers import EnvActions as BaseActions
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class Constants(BaseConstants):
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DIRT = 'Dirt'
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class Actions(BaseActions):
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CLEAN_UP = 'do_cleanup_action'
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a = Actions
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c = Constants
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future_planning = 7
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class TSPDirtAgent(Agent):
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def __init__(self, env, *args,
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@ -26,7 +40,7 @@ class TSPDirtAgent(Agent):
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def predict(self, *_, **__):
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if self._env[c.DIRT].by_pos(self.pos) is not None:
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# Translate the action_object to an integer to have the same output as any other model
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action = h.EnvActions.CLEAN_UP
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action = a.CLEAN_UP
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elif any('door' in x.name.lower() for x in self.tile.guests):
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door = next(x for x in self.tile.guests if 'door' in x.name.lower())
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if door.is_closed:
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@ -37,7 +51,7 @@ class TSPDirtAgent(Agent):
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else:
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action = self._predict_move()
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# Translate the action_object to an integer to have the same output as any other model
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action_obj = next(action_i for action_i, action_obj in enumerate(self._env._actions) if action_obj == action)
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action_obj = next(action_i for action_name, action_i in self._env.named_action_space.items() if action_name == action)
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return action_obj
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def _predict_move(self):
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@ -181,11 +181,11 @@ class BaseFactory(gym.Env):
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if agents_to_spawn:
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agents = Agents.from_tiles(floor.empty_tiles[:agents_to_spawn], self._level_shape, **agents_kwargs)
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else:
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agents = Agents(**agents_kwargs)
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agents = Agents(self._level_shape, **agents_kwargs)
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if self._injected_agents:
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initialized_injections = list()
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for i, injection in enumerate(self._injected_agents):
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agents.register_item(injection(self, floor.empty_tiles[agents_to_spawn+i+1], static_problem=False))
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agents.register_item(injection(self, floor.empty_tiles[0], agents, static_problem=False))
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initialized_injections.append(agents[-1])
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self._initialized_injections = initialized_injections
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self._entities.register_additional_items({c.AGENT: agents})
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@ -335,7 +335,12 @@ class BaseFactory(gym.Env):
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# Generel Observations
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lvl_obs = self[c.WALLS].as_array()
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door_obs = self[c.DOORS].as_array()
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global_agent_obs = self[c.AGENT].as_array() if self.obs_prop.render_agents != a_obs.NOT else None
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if self.obs_prop.render_agents == a_obs.NOT:
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global_agent_obs = None
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elif self.obs_prop.omit_agent_self and self.n_agents == 1:
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global_agent_obs = None
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else:
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global_agent_obs = self[c.AGENT].as_array().copy()
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placeholder_obs = self[c.AGENT_PLACEHOLDER].as_array() if self[c.AGENT_PLACEHOLDER] else None
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add_obs_dict = self._additional_observations()
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@ -343,7 +348,7 @@ class BaseFactory(gym.Env):
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obs_dict = dict()
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# Build Agent Observations
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if self.obs_prop.render_agents != a_obs.NOT:
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if self.obs_prop.omit_agent_self:
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if self.obs_prop.omit_agent_self and self.n_agents >= 2:
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if self.obs_prop.render_agents == a_obs.SEPERATE:
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other_agent_obs_idx = [x for x in range(self.n_agents) if x != agent_idx]
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agent_obs = np.take(global_agent_obs, other_agent_obs_idx, axis=0)
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@ -361,11 +366,12 @@ class BaseFactory(gym.Env):
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lvl_obs += global_agent_obs
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obs_dict[c.WALLS] = lvl_obs
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if self.obs_prop.render_agents in [a_obs.SEPERATE, a_obs.COMBINED]:
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if self.obs_prop.render_agents in [a_obs.SEPERATE, a_obs.COMBINED] and agent_obs is not None:
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obs_dict[c.AGENT] = agent_obs
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if self[c.AGENT_PLACEHOLDER]:
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if self[c.AGENT_PLACEHOLDER] and placeholder_obs is not None:
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obs_dict[c.AGENT_PLACEHOLDER] = placeholder_obs
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obs_dict[c.DOORS] = door_obs
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if self.parse_doors and door_obs is not None:
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obs_dict[c.DOORS] = door_obs
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obs_dict.update(add_obs_dict)
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obsn = np.vstack(list(obs_dict.values()))
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if self.obs_prop.pomdp_r:
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@ -381,20 +387,21 @@ class BaseFactory(gym.Env):
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zip(keys, idxs, list(idxs[1:]) + [idxs[-1]+1, ])}
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# Shadow Casting
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try:
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light_block_obs = [obs_idx for key, obs_idx in per_agent_expl_idx[agent.name].items()
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if self[key].is_blocking_light]
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# Flatten
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light_block_obs = [x for y in light_block_obs for x in y]
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shadowed_obs = [obs_idx for key, obs_idx in per_agent_expl_idx[agent.name].items()
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if self[key].can_be_shadowed]
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# Flatten
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shadowed_obs = [x for y in shadowed_obs for x in y]
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except AttributeError as e:
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print('Check your Keys! Only use Constants as Keys!')
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print(e)
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raise e
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if self.obs_prop.cast_shadows:
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try:
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light_block_obs = [obs_idx for key, obs_idx in per_agent_expl_idx[agent.name].items()
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if self[key].is_blocking_light]
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# Flatten
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light_block_obs = [x for y in light_block_obs for x in y]
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shadowed_obs = [obs_idx for key, obs_idx in per_agent_expl_idx[agent.name].items()
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if self[key].can_be_shadowed]
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# Flatten
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shadowed_obs = [x for y in shadowed_obs for x in y]
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except AttributeError as e:
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print('Check your Keys! Only use Constants as Keys!')
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print(e)
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raise e
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obs_block_light = obsn[light_block_obs] != c.OCCUPIED_CELL
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door_shadowing = False
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if self.parse_doors:
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@ -6,6 +6,7 @@ import random
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import numpy as np
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# from algorithms.TSP_dirt_agent import TSPDirtAgent
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from algorithms.TSP_dirt_agent import TSPDirtAgent
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from environments.helpers import Constants as BaseConstants
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from environments.helpers import EnvActions as BaseActions
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from environments.helpers import Rewards as BaseRewards
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@ -27,9 +28,9 @@ class Actions(BaseActions):
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class Rewards(BaseRewards):
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CLEAN_UP_VALID = 0.5
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CLEAN_UP_FAIL = -0.1
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CLEAN_UP_LAST_PIECE = 4.5
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CLEAN_UP_VALID = 1
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CLEAN_UP_FAIL = -0.1
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CLEAN_UP_LAST_PIECE = 4
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class DirtProperties(NamedTuple):
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@ -293,13 +294,13 @@ if __name__ == '__main__':
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global_timings = []
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for i in range(10):
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factory = DirtFactory(n_agents=4, done_at_collision=False,
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factory = DirtFactory(n_agents=1, done_at_collision=False,
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level_name='rooms', max_steps=1000,
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doors_have_area=False,
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obs_prop=obs_props, parse_doors=True,
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verbose=True,
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mv_prop=move_props, dirt_prop=dirt_props,
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# inject_agents=[TSPDirtAgent],
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inject_agents=[TSPDirtAgent],
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)
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# noinspection DuplicatedCode
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@ -317,10 +318,11 @@ if __name__ == '__main__':
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env_state = factory.reset()
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if render:
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factory.render()
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# tsp_agent = factory.get_injected_agents()[0]
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tsp_agent = factory.get_injected_agents()[0]
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rwrd = 0
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for agent_i_action in random_actions:
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agent_i_action = tsp_agent.predict()
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env_state, step_rwrd, done_bool, info_obj = factory.step(agent_i_action)
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rwrd += step_rwrd
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if render:
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@ -1,14 +1,10 @@
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import warnings
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from pathlib import Path
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import numpy as np
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import yaml
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from stable_baselines3 import A2C
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from stable_baselines3 import A2C, PPO, DQN
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from environments import helpers as h
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from environments.helpers import Constants as c
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from environments.factory.factory_dirt import DirtFactory
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from environments.factory.combined_factories import DirtItemFactory
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from environments.logging.recorder import EnvRecorder
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warnings.filterwarnings('ignore', category=FutureWarning)
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@ -17,7 +13,7 @@ warnings.filterwarnings('ignore', category=UserWarning)
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if __name__ == '__main__':
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determin = True
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determin = False
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render = True
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record = False
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seed = 67
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@ -37,7 +33,7 @@ if __name__ == '__main__':
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this_model = out_path / 'model.zip'
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model_cls = A2C # next(val for key, val in h.MODEL_MAP.items() if key in out_path.parent.name)
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model_cls = PPO # next(val for key, val in h.MODEL_MAP.items() if key in out_path.parent.name)
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models = [model_cls.load(this_model)]
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# Init Env
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@ -114,6 +114,7 @@ if __name__ == '__main__':
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train = True
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individual_run = True
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combined_run = True
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multi_env = False
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train_steps = 2e5
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frames_to_stack = 3
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@ -122,7 +123,7 @@ if __name__ == '__main__':
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study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}'
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def policy_model_kwargs():
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return dict(learning_rate=0.0003, n_steps=10, gamma=0.95, gae_lambda=0.0, ent_coef=0.01, vf_coef=0.5)
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return dict()
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# Define Global Env Parameters
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# Define properties object parameters
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@ -142,22 +143,22 @@ if __name__ == '__main__':
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item_props = ItemProperties(n_items=10, spawn_frequency=30, n_drop_off_locations=2,
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max_agent_inventory_capacity=15)
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dest_props = DestProperties(n_dests=4, spawn_mode=DestModeOptions.GROUPED, spawn_frequency=1)
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factory_kwargs = dict(n_agents=1, max_steps=400, parse_doors=True,
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level_name='rooms', doors_have_area=False,
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factory_kwargs = dict(n_agents=1, max_steps=500, parse_doors=True,
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level_name='rooms', doors_have_area=True,
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verbose=False,
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mv_prop=move_props,
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obs_prop=obs_props,
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done_at_collision=True
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done_at_collision=False
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)
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# Bundle both environments with global kwargs and parameters
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env_map = {}
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env_map.update({'dirt': (DirtFactory, dict(dirt_prop=dirt_props,
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**factory_kwargs.copy()))})
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env_map.update({'item': (ItemFactory, dict(item_prop=item_props,
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**factory_kwargs.copy()))})
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env_map.update({'dest': (DestFactory, dict(dest_prop=dest_props,
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**factory_kwargs.copy()))})
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# env_map.update({'item': (ItemFactory, dict(item_prop=item_props,
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# **factory_kwargs.copy()))})
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# env_map.update({'dest': (DestFactory, dict(dest_prop=dest_props,
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# **factory_kwargs.copy()))})
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env_map.update({'combined': (DirtDestItemFactory, dict(dest_prop=dest_props,
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item_prop=item_props,
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dirt_prop=dirt_props,
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@ -168,7 +169,7 @@ if __name__ == '__main__':
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# Build Major Loop parameters, parameter versions, Env Classes and models
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if train:
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for env_key in (env_key for env_key in env_map if 'combined' != env_key):
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model_cls = h.MODEL_MAP['A2C']
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model_cls = h.MODEL_MAP['PPO']
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combination_path = study_root_path / env_key
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env_class, env_kwargs = env_map[env_key]
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@ -177,8 +178,11 @@ if __name__ == '__main__':
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continue
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combination_path.mkdir(parents=True, exist_ok=True)
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env_factory = SubprocVecEnv([encapsule_env_factory(env_class, env_kwargs)
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for _ in range(6)], start_method="spawn")
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if not multi_env:
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env_factory = encapsule_env_factory(env_class, env_kwargs)()
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else:
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env_factory = SubprocVecEnv([encapsule_env_factory(env_class, env_kwargs)
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for _ in range(6)], start_method="spawn")
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param_path = combination_path / f'env_params.json'
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try:
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