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https://github.com/illiumst/marl-factory-grid.git
synced 2025-06-21 11:21:35 +02:00
Debugging and collision rendering
This commit is contained in:
Steffen Illium
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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