def test_vec_env(spec):
"""Test that our {Dummy,Subproc}VecMultiEnv gives the same results as
each other."""
env_fns = [lambda: make_env(spec, i) for i in range(4)]
venv1 = multi_agent.make_dummy_vec_multi_env(env_fns)
venv2 = multi_agent.make_subproc_vec_multi_env(env_fns)
is_multicomp = spec.id.startswith("multicomp/")
# Can't easily compare info dicts returned by multicomp/ environments, so just skip that check
assert_envs_equal(venv1, venv2, 100, check_info=not is_multicomp)
def create_multi_agent_curried_policy_wrapper(mon_dir,
env_name,
num_envs,
embed_index,
max_steps,
state_shape=None,
add_zoo=False,
num_zoo=5):
def episode_limit(env):
return time_limit.TimeLimit(env, max_episode_steps=max_steps)
def env_fn(i):
return make_env(env_name,
seed=42,
i=i,
out_dir=mon_dir,
pre_wrappers=[episode_limit])
vec_env = make_dummy_vec_multi_env(
[lambda: env_fn(i) for i in range(num_envs)])
zoo = load_policy(
policy_path="1",
policy_type="zoo",
env=vec_env,
env_name=env_name,
index=1 - embed_index,
transparent_params=None,
)
half_env = FakeSingleSpacesVec(vec_env, agent_id=embed_index)
policies = [
_get_constant_policy(half_env,
constant_value=half_env.action_space.sample(),
state_shape=state_shape) for _ in range(10)
]
if add_zoo:
policies += [zoo] * num_zoo
policy_wrapper = MultiPolicyWrapper(policies=policies, num_envs=num_envs)
vec_env = CurryVecEnv(venv=vec_env,
policy=policy_wrapper,
agent_idx=embed_index,
deterministic=False)
vec_env = FlattenSingletonVecEnv(vec_env)
yield vec_env, policy_wrapper, zoo
policy_wrapper.close()
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(prefix="score-videos")
save_dir = tmp_dir.name
else:
tmp_dir = None
video_dirs = [osp.join(save_dir, str(i)) for i in range(num_env)]
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, 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:
added = False
for file_path in os.listdir(env_video_dir):
added |= _save_video_or_metadata(env_video_dir, file_path)
if not added:
raise FileNotFoundError(
f"No video artifacts found in path {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
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)
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
