def get_obs_shape(obs_space):
obs_shape = AttrDict()
if hasattr(obs_space, 'spaces'):
for key, space in obs_space.spaces.items():
obs_shape[key] = space.shape
else:
obs_shape.obs = obs_space.shape
return obs_shape
def test_performance(self):
params = AgentPPO.Params('test_performance')
params.ppo_epochs = 2
params.rollout = 16
env = make_doom_env(doom_env_by_name(TEST_ENV_NAME))
observation_shape = env.observation_space.shape
experience_size = params.num_envs * params.rollout
# generate random data
data = AttrDict()
data.obs = np.random.normal(size=(experience_size, ) +
observation_shape)
data.act = np.random.randint(0, 3, size=[experience_size])
data.old_prob = np.random.uniform(0, 1, size=[experience_size])
data.adv = np.random.normal(size=[experience_size])
data.ret = np.random.normal(size=[experience_size])
self.train_feed_dict(env, data, params, use_gpu=False)
self.train_feed_dict(env, data, params, use_gpu=True)
self.train_dataset(env, data, params, use_gpu=False)
self.train_dataset(env, data, params, use_gpu=True)
env.close()