Debugging and collision rendering
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Steffen Illium
parent
ab9cde7853
commit
6287380f60
@ -494,10 +494,10 @@ class BaseFactory(gym.Env):
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if self._actions.is_moving_action(agent.temp_action):
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if agent.temp_valid:
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# info_dict.update(movement=1)
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reward -= 0.001
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reward -= 0.01
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pass
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else:
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reward -= 0.01
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reward -= 0.05
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self.print(f'{agent.name} just hit the wall at {agent.pos}.')
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per_agent_info_dict[agent.name].update({f'{agent.name}_vs_LEVEL': 1})
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@ -65,7 +65,7 @@ class DirtRegister(MovingEntityObjectRegister):
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def as_array(self):
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if self._array is not None:
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self._array[:] = c.FREE_CELL.value
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for dirt in self.values():
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for dirt in list(self.values()):
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if dirt.amount == 0:
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self.delete_item(dirt)
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self._array[0, dirt.x, dirt.y] = dirt.amount
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@ -136,7 +136,7 @@ def asset_str(agent):
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# if any([x is None for x in [self._slices[j] for j in agent.collisions]]):
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# print('error')
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col_names = [x.name for x in agent.temp_collisions]
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if c.AGENT.value in col_names:
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if any(c.AGENT.value in name for name in col_names):
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return 'agent_collision', 'blank'
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elif not agent.temp_valid or c.LEVEL.name in col_names or c.AGENT.name in col_names:
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return c.AGENT.value, 'invalid'
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@ -16,11 +16,11 @@ warnings.filterwarnings('ignore', category=UserWarning)
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if __name__ == '__main__':
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model_name = 'DQN_163519000'
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model_name = 'A2C_ItsDirt'
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run_id = 0
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seed = 69
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n_agents = 2
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out_path = Path('debug_out/DQN_163519000/1_DQN_163519000')
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seed = 67
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n_agents = 1
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out_path = Path('study_out/e_1_ItsDirt/no_obs/dirt/A2C_ItsDirt/0_A2C_ItsDirt')
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model_path = out_path
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with (out_path / f'env_params.json').open('r') as f:
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@ -46,10 +46,17 @@ if __name__ == '__main__':
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env_state = env.reset()
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rew, done_bool = 0, False
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while not done_bool:
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actions = [model.predict(
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np.stack([env_state[i][j] for i in range(env_state.shape[0])]),
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deterministic=False)[0] for j, model in enumerate(models)]
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if n_agents > 1:
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actions = [model.predict(
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np.stack([env_state[i][j] for i in range(env_state.shape[0])]),
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deterministic=True)[0] for j, model in enumerate(models)]
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else:
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actions = models[0].predict(env_state, deterministic=True)[0]
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if any([agent.pos in [door.pos for door in env.unwrapped[c.DOORS]]
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for agent in env.unwrapped[c.AGENT]]):
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print('On Door')
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env_state, step_r, done_bool, info_obj = env.step(actions)
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recorder.read_info(0, info_obj)
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rew += step_r
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env.render()
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155
studies/e_1.py
155
studies/e_1.py
@ -147,7 +147,7 @@ def load_model_run_study(seed_path, env_to_run, additional_kwargs_dict):
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try:
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actions = [model.predict(
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np.stack([env_state[i][j] for i in range(env_state.shape[0])]),
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deterministic=False)[0] for j, model in enumerate(models)]
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deterministic=True)[0] for j, model in enumerate(models)]
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except ValueError as e:
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print(e)
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print('Env_Kwargs are:\n')
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@ -169,10 +169,11 @@ def load_model_run_study(seed_path, env_to_run, additional_kwargs_dict):
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if __name__ == '__main__':
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train_steps = 8e5
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train_steps = 5e6
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n_seeds = 3
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# Define a global studi save path
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start_time = '900000' # int(time.time())
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start_time = 'Now_with_doors' # int(time.time())
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study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}_{start_time}'
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# Define Global Env Parameters
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@ -195,57 +196,95 @@ if __name__ == '__main__':
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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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factory_kwargs = dict(n_agents=1, max_steps=400, parse_doors=True,
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level_name='rooms', record_episodes=False, doors_have_area=False,
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level_name='rooms', record_episodes=False, 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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)
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# Bundle both environments with global kwargs and parameters
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env_map = {'dirt': (DirtFactory, dict(dirt_prop=dirt_props,
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**factory_kwargs.copy())),
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'item': (ItemFactory, dict(item_prop=item_props,
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**factory_kwargs.copy())),
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'itemdirt': (DirtItemFactory, dict(dirt_prop=dirt_props,
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item_prop=item_props,
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**factory_kwargs.copy()))}
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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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if False:
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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({'itemdirt': (DirtItemFactory, dict(dirt_prop=dirt_props, item_prop=item_props,
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**factory_kwargs.copy()))})
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env_names = list(env_map.keys())
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# Define parameter versions according with #1,2[1,0,N],3
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observation_modes = {
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# Fill-value = 0
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# DEACTIVATED 'seperate_0': dict(additional_env_kwargs=dict(additional_agent_placeholder=0)),
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# Fill-value = 1
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# DEACTIVATED 'seperate_1': dict(additional_env_kwargs=dict(additional_agent_placeholder=1)),
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# Fill-value = N(0, 1)
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'seperate_N': dict(
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post_training_kwargs=
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dict(obs_prop=ObservationProperties(
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render_agents=AgentRenderOptions.COMBINED,
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additional_agent_placeholder=None,
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omit_agent_self=True,
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frames_to_stack=3,
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pomdp_r=2)
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),
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additional_env_kwargs=
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dict(obs_prop=ObservationProperties(
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render_agents=AgentRenderOptions.NOT,
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additional_agent_placeholder='N',
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omit_agent_self=True,
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frames_to_stack=3,
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pomdp_r=2)
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)
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),
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'in_lvl_obs': dict(
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post_training_kwargs=
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dict(obs_prop=ObservationProperties(
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render_agents=AgentRenderOptions.LEVEL,
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omit_agent_self=True,
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additional_agent_placeholder=None,
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frames_to_stack=3,
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pomdp_r=2)
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)
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),
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observation_modes = {}
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if False:
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observation_modes.update({
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'seperate_1': dict(
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post_training_kwargs=
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dict(obs_prop=ObservationProperties(
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render_agents=AgentRenderOptions.COMBINED,
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additional_agent_placeholder=None,
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omit_agent_self=True,
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frames_to_stack=3,
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pomdp_r=2)
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),
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additional_env_kwargs=
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dict(obs_prop=ObservationProperties(
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render_agents=AgentRenderOptions.NOT,
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additional_agent_placeholder=1,
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omit_agent_self=True,
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frames_to_stack=3,
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pomdp_r=2)
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)
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)})
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observation_modes.update({
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'seperate_0': dict(
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post_training_kwargs=
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dict(obs_prop=ObservationProperties(
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render_agents=AgentRenderOptions.COMBINED,
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additional_agent_placeholder=None,
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omit_agent_self=True,
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frames_to_stack=3,
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pomdp_r=2)
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),
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additional_env_kwargs=
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dict(obs_prop=ObservationProperties(
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render_agents=AgentRenderOptions.NOT,
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additional_agent_placeholder=0,
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omit_agent_self=True,
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frames_to_stack=3,
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pomdp_r=2)
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)
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)})
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observation_modes.update({
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'seperate_N': dict(
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post_training_kwargs=
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dict(obs_prop=ObservationProperties(
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render_agents=AgentRenderOptions.COMBINED,
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additional_agent_placeholder=None,
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omit_agent_self=True,
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frames_to_stack=3,
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pomdp_r=2)
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),
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additional_env_kwargs=
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dict(obs_prop=ObservationProperties(
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render_agents=AgentRenderOptions.NOT,
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additional_agent_placeholder='N',
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omit_agent_self=True,
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frames_to_stack=3,
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pomdp_r=2)
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)
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)})
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observation_modes.update({
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'in_lvl_obs': dict(
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post_training_kwargs=
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dict(obs_prop=ObservationProperties(
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render_agents=AgentRenderOptions.LEVEL,
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omit_agent_self=True,
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additional_agent_placeholder=None,
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frames_to_stack=3,
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pomdp_r=2)
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)
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)})
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observation_modes.update({
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# No further adjustment needed
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'no_obs': dict(
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post_training_kwargs=
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@ -257,14 +296,14 @@ if __name__ == '__main__':
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pomdp_r=2)
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)
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)
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}
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})
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# Train starts here ############################################################
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# Build Major Loop parameters, parameter versions, Env Classes and models
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if True:
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for obs_mode in observation_modes.keys():
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for env_name in env_names:
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for model_cls in [h.MODEL_MAP['A2C'], h.MODEL_MAP['DQN']]:
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for model_cls in [h.MODEL_MAP['A2C']]:
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# Create an identifier, which is unique for every combination and easy to read in filesystem
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identifier = f'{model_cls.__name__}_{start_time}'
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# Train each combination per seed
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@ -274,7 +313,7 @@ if __name__ == '__main__':
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# Retrieve and set the observation mode specific env parameters
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additional_kwargs = observation_modes.get(obs_mode, {}).get("additional_env_kwargs", {})
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env_kwargs.update(additional_kwargs)
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for seed in range(5):
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for seed in range(n_seeds):
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env_kwargs.update(env_seed=seed)
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# Output folder
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seed_path = combination_path / f'{str(seed)}_{identifier}'
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@ -352,6 +391,7 @@ if __name__ == '__main__':
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# Evaluation starts here #####################################################
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# First Iterate over every model and monitor "as trained"
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if True:
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print('Start Baseline Tracking')
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for obs_mode in observation_modes:
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obs_mode_path = next(x for x in study_root_path.iterdir() if x.is_dir() and x.name == obs_mode)
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# For trained policy in study_root_path / identifier
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@ -370,9 +410,11 @@ if __name__ == '__main__':
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# for seed_path in (y for y in policy_path.iterdir() if y.is_dir()):
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# load_model_run_baseline(seed_path)
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print('Baseline Tracking done')
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# Then iterate over every model and monitor "ood behavior" - "is it ood?"
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if True:
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print('Start OOD Tracking')
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for obs_mode in observation_modes:
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obs_mode_path = next(x for x in study_root_path.iterdir() if x.is_dir() and x.name == obs_mode)
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# For trained policy in study_root_path / identifier
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@ -387,18 +429,19 @@ if __name__ == '__main__':
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pool = mp.Pool(mp.cpu_count())
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paths = list(y for y in policy_path.iterdir() if y.is_dir() \
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and not (y / ood_monitor_file).exists())
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result = pool.starmap(load_model_run_study,
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it.product(paths,
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(env_map[env_path.name][0],),
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(observation_modes[obs_mode],))
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)
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# for seed_path in (y for y in policy_path.iterdir() if y.is_dir()):
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# load_model_run_study(seed_path)
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# result = pool.starmap(load_model_run_study,
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# it.product(paths,
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# (env_map[env_path.name][0],),
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# (observation_modes[obs_mode],))
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# )
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for seed_path in (y for y in policy_path.iterdir() if y.is_dir()):
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load_model_run_study(seed_path, env_map[env_path.name][0], observation_modes[obs_mode])
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print('OOD Tracking Done')
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# Plotting
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if True:
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# TODO: Plotting
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print('Start Plotting')
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for observation_folder in (x for x in study_root_path.iterdir() if x.is_dir()):
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df_list = list()
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for env_folder in (x for x in observation_folder.iterdir() if x.is_dir()):
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