def allPolicyFiles(log_dir):
"""
:param log_dir:
:return:
"""
train_args, algo_name, algo_class, srl_model_path, env_kwargs = loadConfigAndSetup(
log_dir)
files = glob.glob(os.path.join(log_dir + algo_name + '_*_model.pkl'))
printYellow(log_dir)
files = glob.glob(log_dir + '/model_*')
files_list = []
for file in files:
eps = int((file.split('_')[-1]))
files_list.append((eps, file + '/'))
def sortFirst(val):
"""
:param val:
:return:
"""
return val[0]
files_list.sort(key=sortFirst)
res = np.array(files_list)
return res[:, 0], res[:, 1]
def policyCrossEval(log_dir,
task,
episode,
model_path,
num_timesteps=2000,
num_cpu=1,
seed=0):
train_args, algo_name, algo_class, srl_model_path, env_kwargs = loadConfigAndSetup(
log_dir)
env_kwargs = EnvsKwargs(task, env_kwargs)
OK = True
if (not OK):
# no latest model saved yet
return None, False
else:
pass
printGreen(
"Evaluation from the model saved at: {}, with evaluation time steps: {}"
.format(model_path, num_timesteps))
log_dir, environment, algo_args = createEnv(log_dir,
train_args,
algo_name,
algo_class,
env_kwargs,
num_cpu=num_cpu,
seed=seed)
reward = policyEval(environment, model_path, log_dir, algo_class,
algo_args, num_timesteps, num_cpu)
# Just a trick to save the episode number of the reward,but need a little bit more space to store
reward = np.append(episode, reward)
return reward, True
def newPolicy(episodes, file_path):
"""
:param episodes:
:param file_path:
:return:
"""
train_args, algo_name, algo_class, srl_model_path, env_kwargs = loadConfigAndSetup(
file_path)
episode, model_path, OK = latestPolicy(file_path, algo_name)
if episode in episodes:
return -1, '', False
else:
return episode, model_path, True
def policyCrossEval(log_dir,
task,
episode,
model_path,
num_timesteps=2000,
num_cpu=1):
"""
To do a cross evaluation for a certain policy for different tasks
A version of real time evaluation but with some bugs to fix
:param log_dir:
:param task:
:param episode:
:param model_path:
:param num_timesteps: How many timesteps to evaluate the policy
:param num_cpu:
:return:
"""
train_args, algo_name, algo_class, srl_model_path, env_kwargs = loadConfigAndSetup(
log_dir)
env_kwargs = EnvsKwargs(task, env_kwargs)
OK = True
if (not OK):
# no latest model saved yet
return None, False
else:
pass
printGreen(
"Evaluation from the model saved at: {}, with evaluation time steps: {}"
.format(model_path, num_timesteps))
log_dir, environment, algo_args = createEnv(log_dir,
train_args,
algo_name,
algo_class,
env_kwargs,
num_cpu=num_cpu)
reward = policyEval(environment, model_path, log_dir, algo_class,
algo_args, num_timesteps, num_cpu)
# Just a trick to save the episode number of the reward,but need a little bit more space to store
reward = np.append(episode, reward)
return reward, True