def __init__(self, venv, env_name, use_debug, victim_index, victim_path,
victim_type, transparent_params, lb_mul, lb_num, lb_path,
lb_type):
super().__init__(venv)
self.lb_num = lb_num
self.lb_mul = lb_mul
if transparent_params is None:
raise ValueError(
"LookbackRewardVecWrapper assumes transparent policies and venvs."
)
self.transparent_params = transparent_params
self.victim_index = victim_index
self._policy = load_policy(lb_type,
lb_path,
self.venv.unwrapped,
env_name,
1 - victim_index,
transparent_params=None)
self._action = None
self._obs = None
self._state = None
self._new_lb_state = None
self._dones = [False] * self.num_envs
self.ep_lens = np.zeros(self.num_envs).astype(int)
self.lb_tuples = self._create_lb_tuples(env_name, use_debug,
victim_index, victim_path,
victim_type)
self.use_debug = use_debug
if self.use_debug:
# create a debug file for this venv and also every lookback venv ordinally
self.debug_files = [
open(f'debug{i}.pkl', 'wb') for i in range(self.lb_num + 1)
]
self.get_debug_venv().set_debug_file(self.debug_files[0])
def _create_lb_tuples(self, env_name, use_debug, victim_index, victim_path,
victim_type):
"""Create lookback data structures which are used to compare our episode rollouts against
those of an environment where a lookback base policy acted instead.
params victim_index, victim_path, victim_type are the same as in policy_loader.load_policy
:param use_debug (bool): Use DummyVecEnv instead of SubprocVecEnv
:return: (list<LookbackTuple>) lb_tuples
"""
def env_fn(i):
return make_env(
env_name,
0,
i,
out_dir='data/lookbacks/',
pre_wrappers=[GymCompeteToOurs, OldMujocoResettableWrapper])
lb_tuples = []
for _ in range(self.lb_num):
make_vec_env = make_dummy_vec_multi_env if use_debug else make_subproc_vec_multi_env
multi_venv = make_vec_env(
[lambda: env_fn(i) for i in range(self.num_envs)])
if use_debug:
multi_venv = DebugVenv(multi_venv)
victim = load_policy(policy_path=victim_path,
policy_type=victim_type,
env=multi_venv,
env_name=env_name,
index=victim_index,
transparent_params=self.transparent_params)
multi_venv = EmbedVictimWrapper(multi_env=multi_venv,
victim=victim,
victim_index=victim_index,
transparent=True,
deterministic=True)
single_venv = FlattenSingletonVecEnv(multi_venv)
data_dict = {
'state': None,
'action': None,
'info': defaultdict(dict)
}
lb_tuples.append(LookbackTuple(venv=single_venv, data=data_dict))
return lb_tuples
def wrap_adv_noise_ball(env_name, our_idx, multi_venv, adv_noise_params,
victim_path, victim_type, deterministic):
adv_noise_agent_val = adv_noise_params['noise_val']
base_policy_path = adv_noise_params['base_path']
base_policy_type = adv_noise_params['base_type']
base_policy = load_policy(policy_path=base_policy_path,
policy_type=base_policy_type,
env=multi_venv,
env_name=env_name,
index=our_idx)
base_action_space = multi_venv.action_space.spaces[our_idx]
adv_noise_action_space = Box(
low=adv_noise_agent_val * base_action_space.low,
high=adv_noise_agent_val * base_action_space.high)
multi_venv = MergeAgentVecEnv(venv=multi_venv,
policy=base_policy,
replace_action_space=adv_noise_action_space,
merge_agent_idx=our_idx,
deterministic=deterministic)
return multi_venv
def maybe_embed_victim(multi_venv, our_idx, scheduler, log_callbacks, env_name,
victim_type, victim_path, victim_index, victim_noise,
victim_noise_params, adv_noise_params,
transparent_params, lookback_params):
if victim_type != 'none':
deterministic = lookback_params is not None
# If we are actually training an epsilon-ball noise agent on top of a zoo agent
if adv_noise_params['noise_val'] is not None:
multi_venv = wrap_adv_noise_ball(env_name,
our_idx,
multi_venv,
deterministic=deterministic)
# Load the victim and then wrap it if appropriate.
victim = load_policy(policy_path=victim_path,
policy_type=victim_type,
env=multi_venv,
env_name=env_name,
index=victim_index,
transparent_params=transparent_params)
if victim_noise:
victim = apply_victim_wrapper(victim=victim,
noise_params=victim_noise_params,
scheduler=scheduler)
log_callbacks.append(
lambda logger, locals, globals: victim.log_callback(logger))
# Curry the victim
transparent = transparent_params is not None
multi_venv = EmbedVictimWrapper(multi_env=multi_venv,
victim=victim,
victim_index=victim_index,
transparent=transparent,
deterministic=deterministic)
return multi_venv
def score_agent(_run, _seed, env_name, agent_a_path, agent_b_path, agent_a_type, agent_b_type,
record_traj, record_traj_params, transparent_params, num_env,
videos, video_params, mask_agent_index, noisy_agent_index,
noisy_agent_magnitude, mask_agent_noise):
save_dir = video_params['save_dir']
if videos:
if save_dir is None:
score_ex_logger.info("No directory provided for saving videos; using a tmpdir instead,"
"but videos will be saved to Sacred run directory")
tmp_dir = tempfile.TemporaryDirectory()
save_dir = tmp_dir.name
else:
tmp_dir = None
video_dirs = [osp.join(save_dir, str(i)) for i in range(num_env)]
pre_wrappers = [GymCompeteToOurs] if 'multicomp' in env_name else []
agent_wrappers = {}
if mask_agent_index is not None:
mask_agent_kwargs = {}
if mask_agent_noise is not None:
mask_agent_kwargs['noise_magnitude'] = mask_agent_noise
agent_wrappers = make_mask_agent_wrappers(env_name, mask_agent_index, **mask_agent_kwargs)
video_params = utils.sacred_copy(video_params) # Sacred issue #499
def env_fn(i):
env = make_env(env_name, _seed, i, None,
pre_wrappers=pre_wrappers,
agent_wrappers=agent_wrappers)
if videos:
if video_params['annotated']:
if 'multicomp' in env_name:
assert num_env == 1, "pretty videos requires num_env=1"
env = AnnotatedGymCompete(env, env_name, agent_a_type, agent_a_path,
agent_b_type, agent_b_path, mask_agent_index,
**video_params['annotation_params'])
else:
warnings.warn(f"Annotated videos not supported for environment '{env_name}'")
env = VideoWrapper(env, video_dirs[i], video_params['single_file'])
return env
env_fns = [functools.partial(env_fn, i) for i in range(num_env)]
if num_env > 1:
venv = make_subproc_vec_multi_env(env_fns)
else:
venv = make_dummy_vec_multi_env(env_fns)
if record_traj:
venv = TrajectoryRecorder(venv, record_traj_params['agent_indices'])
if venv.num_agents == 1 and agent_b_path != 'none':
raise ValueError("Set agent_b_path to 'none' if environment only uses one agent.")
agent_paths = [agent_a_path, agent_b_path]
agent_types = [agent_a_type, agent_b_type]
zipped = list(zip(agent_types, agent_paths))
agents = [load_policy(policy_type, policy_path, venv, env_name, i, transparent_params)
for i, (policy_type, policy_path) in enumerate(zipped[:venv.num_agents])]
if noisy_agent_index is not None:
agents[noisy_agent_index] = NoisyAgentWrapper(agents[noisy_agent_index],
noise_annealer=lambda: noisy_agent_magnitude)
score = get_empirical_score(venv, agents)
for agent in agents:
if agent.sess is not None:
agent.sess.close()
if record_traj:
save_paths = venv.save(save_dir=record_traj_params['save_dir'])
for save_path in save_paths:
score_ex.add_artifact(save_path, name="victim_activations.npz")
venv.close()
if videos:
for env_video_dir in video_dirs:
try:
for file_path in os.listdir(env_video_dir):
_save_video_or_metadata(env_video_dir, file_path)
except FileNotFoundError:
warnings.warn("Can't find path {}; no videos from that path added as artifacts"
.format(env_video_dir))
if tmp_dir is not None:
tmp_dir.cleanup()
for observer in score_ex.observers:
if hasattr(observer, 'dir'):
_clean_video_directory_structure(observer)
return score