def build_env(out_dir, _seed, env_name, num_env, victim_type, victim_index,
mask_victim, mask_victim_kwargs, debug):
pre_wrapper = GymCompeteToOurs if env_name.startswith('multicomp/') else None
agent_wrappers = {}
if mask_victim:
agent_wrappers = make_mask_agent_wrappers(env_name, victim_index, **mask_victim_kwargs)
if victim_type == 'none':
our_idx = 0
else:
our_idx = 1 - victim_index
def env_fn(i):
return utils.make_env(env_name, _seed, i, out_dir, our_idx,
pre_wrapper=pre_wrapper, agent_wrappers=agent_wrappers)
if not debug and num_env > 1:
make_vec_env = make_subproc_vec_multi_env
else:
make_vec_env = make_dummy_vec_multi_env
multi_venv = make_vec_env([functools.partial(env_fn, i) for i in range(num_env)])
if victim_type == 'none':
assert multi_venv.num_agents == 1, "No victim only works in single-agent environments"
else:
assert multi_venv.num_agents == 2, "Need two-agent environment when victim"
return multi_venv, our_idx
def build_env(out_dir, _seed, env_name, num_env, victim_type, victim_index,
mask_victim, mask_victim_kwargs, lookback_params, debug):
pre_wrappers = []
if env_name.startswith('multicomp/'):
pre_wrappers.append(GymCompeteToOurs)
if lookback_params['lb_num'] > 0:
pre_wrappers.append(OldMujocoResettableWrapper)
agent_wrappers = {}
if mask_victim:
agent_wrappers = make_mask_agent_wrappers(env_name, victim_index,
**mask_victim_kwargs)
if victim_type == 'none':
our_idx = 0
else:
our_idx = 1 - victim_index
def env_fn(i):
return modelfree.envs.wrappers.make_env(env_name,
_seed,
i,
out_dir,
our_idx,
pre_wrappers=pre_wrappers,
agent_wrappers=agent_wrappers)
if not debug and num_env > 1:
make_vec_env = make_subproc_vec_multi_env
else:
make_vec_env = make_dummy_vec_multi_env
multi_venv = make_vec_env(
[functools.partial(env_fn, i) for i in range(num_env)])
if debug and lookback_params['lb_num'] > 0:
multi_venv = DebugVenv(multi_venv)
if victim_type == 'none':
assert multi_venv.num_agents == 1, "No victim only works in single-agent environments"
else:
assert multi_venv.num_agents == 2, "Need two-agent environment when victim"
return multi_venv, our_idx
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