def trained_disturber(variant):
env_name = variant['env_name']
env = get_env_from_name(env_name)
env_params = variant['env_params']
eval_params = variant['eval_params']
policy_params = variant['alg_params']
disturber_params = variant['disturber_params']
build_func = get_policy(variant['algorithm_name'])
if 'Fetch' in env_name or 'Hand' in env_name:
s_dim = env.observation_space.spaces['observation'].shape[0] \
+ env.observation_space.spaces['achieved_goal'].shape[0] + \
env.observation_space.spaces['desired_goal'].shape[0]
else:
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.shape[0]
d_dim = env_params['disturbance dim']
policy = build_func(a_dim, s_dim, d_dim, policy_params)
disturbance_chanel_list = np.nonzero(
disturber_params['disturbance_magnitude'])[0]
disturber_params['disturbance_chanel_list'] = disturbance_chanel_list
disturber = Disturber(d_dim, s_dim, disturber_params)
disturber.restore(eval_params['path'])
log_path = variant['log_path'] + '/eval/trained_disturber'
variant['eval_params'].update({'magnitude': 0})
logger.configure(dir=log_path, format_strs=['csv'])
diagnostic_dict, _ = evaluation(variant, env, policy, disturber)
string_to_print = []
[
string_to_print.extend(
[key, ':', str(round(diagnostic_dict[key], 2)), '|'])
for key in diagnostic_dict.keys()
]
print(''.join(string_to_print))
[logger.logkv(key, diagnostic_dict[key]) for key in diagnostic_dict.keys()]
logger.dumpkvs()
def trained_disturber(variant):
env_name = variant["env_name"]
env = get_env_from_name(env_name)
env_params = variant["env_params"]
eval_params = variant["eval_params"]
policy_params = variant["alg_params"]
disturber_params = variant["disturber_params"]
build_func = get_policy(variant["algorithm_name"])
if "Fetch" in env_name or "Hand" in env_name:
s_dim = (env.observation_space.spaces["observation"].shape[0] +
env.observation_space.spaces["achieved_goal"].shape[0] +
env.observation_space.spaces["desired_goal"].shape[0])
else:
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.shape[0]
d_dim = env_params["disturbance dim"]
policy = build_func(a_dim, s_dim, d_dim, policy_params)
disturbance_chanel_list = np.nonzero(
disturber_params["disturbance_magnitude"])[0]
disturber_params["disturbance_chanel_list"] = disturbance_chanel_list
disturber = Disturber(d_dim, s_dim, disturber_params)
disturber.restore(eval_params["path"])
log_path = variant["log_path"] + "/eval/trained_disturber"
variant["eval_params"].update({"magnitude": 0})
logger.configure(dir=log_path, format_strs=["csv"])
diagnostic_dict, _ = evaluation(variant, env, policy, disturber)
string_to_print = []
[
string_to_print.extend(
[key, ":", str(round(diagnostic_dict[key], 2)), "|"])
for key in diagnostic_dict.keys()
]
print("".join(string_to_print))
[logger.logkv(key, diagnostic_dict[key]) for key in diagnostic_dict.keys()]
logger.dumpkvs()