class Tester(object):
def __init__(self, make_env, hps, num_timesteps, envs_per_process):
self.make_env = make_env
self.hps = hps
self.envs_per_process = envs_per_process
self.num_timesteps = num_timesteps
self._set_env_vars(
) # 初始化 ob_space,ac_space,ob_mean,ob_std, 初始化 self.envs 包含多个环境模型
self.policy = CnnPolicy(scope='pol',
ob_space=self.ob_space,
ac_space=self.ac_space,
hidsize=512,
feat_dim=512,
ob_mean=self.ob_mean,
ob_std=self.ob_std,
layernormalize=False,
nl=tf.nn.leaky_relu)
self.feature_extractor = FeatureExtractor(
policy=self.policy,
features_shared_with_policy=False,
feat_dim=512,
layernormalize=hps['layernorm'])
# 初始化 环境模型 的类. 上述定义的 feature_extractor 将作为一个参数传入
self.dynamics = DvaeDynamics(auxiliary_task=self.feature_extractor,
reward_type=hps['reward_type'],
sample_seeds=hps['sample_seeds'])
self.agent = PpoOptimizer(scope='ppo',
ob_space=self.ob_space,
ac_space=self.ac_space,
stochpol=self.policy,
use_news=hps['use_news'],
gamma=hps['gamma'],
lam=hps["lambda"],
nepochs=hps['nepochs'],
nminibatches=hps['nminibatches'],
lr=hps['lr'],
cliprange=0.1,
nsteps_per_seg=hps['nsteps_per_seg'],
nsegs_per_env=hps['nsegs_per_env'],
ent_coef=hps['ent_coeff'],
normrew=hps['norm_rew'],
normadv=hps['norm_adv'],
ext_coeff=hps['ext_coeff'],
int_coeff=hps['int_coeff'],
dynamics=self.dynamics,
nepochs_dvae=0)
# agent 损失: 包括 actor,critic,entropy 损失; 先在加上 feature 学习时包含的损失
self.agent.to_report['aux'] = tf.reduce_mean(
self.feature_extractor.loss)
self.agent.total_loss += self.agent.to_report['aux']
# dynamic 损失, 将所有 dynamic 的损失累加起来
self.agent.to_report['dyn_loss'] = tf.reduce_mean(self.dynamics.loss)
self.agent.total_loss += self.agent.to_report['dyn_loss']
# 计算状态经过辅助任务提取特征的方差, shape=(512,), 下面取 tf.reduce_mean 后是一个标量
self.agent.to_report['feat_var'] = tf.reduce_mean(
tf.nn.moments(self.feature_extractor.features, [0, 1])[1])
def _set_env_vars(self):
"""
该 env 仅是为了初始化 ob_space, ac_space, ob_mean, ob_std. 因此在算完之后 del 掉.
随后初始化 self.envs_per_process 个 env
"""
env = self.make_env(0)
# ob_space.shape=(84, 84, 4) ac_space.shape=Discrete(4)
self.ob_space, self.ac_space = env.observation_space, env.action_space
# 随机智能体与环境交互, 计算观测的均值和标准差. ob_mean.shape=(84,84,4), 是0-255之间的数. ob_std是标量, breakout中为 1.8
self.ob_mean, self.ob_std = random_agent_ob_mean_std(env)
if self.hps["env_kind"] == "unity":
env.close()
del env
self.envs = [
functools.partial(self.make_env, i)
for i in range(self.envs_per_process)
]
def play(self, tf_sess, args_tmp, saver, model_path):
print("model_path: ", model_path)
with tf_sess.as_default():
print("Load wights..")
saver.restore(tf_sess, model_path)
print("Load done.")
# rollout
env = self.make_env(0)
max_reward = -10000.
for i in range(5):
obs = env.reset()
rews, frames = [], []
while True:
obs = np.expand_dims(np.squeeze(obs), axis=0)
assert obs.shape == (1, 84, 84, 4)
acs, vpreds, nlps = self.policy.get_ac_value_nlp(obs)
obs, rew, done, info = env.step(acs[0])
rews.append(rew)
obs = np.array(obs)
frames.append(env.render(mode='rgb_array'))
if done:
break
if max_reward < np.sum(rews):
max_reward = np.sum(rews)
print("Max rewards:", max_reward)
save_np_as_mp4(
frames,
"/Users/bai/Desktop/video/" + args_tmp['env'] + '.mp4')
class Scorer(object):
def __init__(self, make_env, hps, num_timesteps, envs_per_process):
self.make_env = make_env
self.hps = hps
self.envs_per_process = envs_per_process
self.num_timesteps = num_timesteps
# self._set_env_vars()
self.ob_mean, self.ob_std, self.ob_space, self.ac_space = random_agent_ob_mean_std(
None, hps['env'], nsteps=1, load=True)
# env = self.make_env(256, add_monitor=False, sleep_multiple=1./32)
# self.ob_space, self.ac_space = env.observation_space, env.action_space
# env.close()
# del env
self.envs = [
functools.partial(self.make_env, i + 256 + 1)
for i in range(envs_per_process)
]
self.policy = CnnPolicy(scope='pol',
ob_space=self.ob_space,
ac_space=self.ac_space,
hidsize=512,
feat_dim=512,
ob_mean=self.ob_mean,
ob_std=self.ob_std,
layernormalize=False,
nl=tf.nn.leaky_relu)
self.feature_extractor = {
"none": FeatureExtractor,
"idf": InverseDynamics,
"vaesph": partial(VAE, spherical_obs=True),
"vaenonsph": partial(VAE, spherical_obs=False),
"pix2pix": JustPixels
}[hps['feat_learning']]
self.feature_extractor = self.feature_extractor(
policy=self.policy,
features_shared_with_policy=False,
feat_dim=512,
layernormalize=hps['layernorm'])
self.dynamics = Dynamics if hps['feat_learning'] != 'pix2pix' else UNet
self.dynamics = self.dynamics(
auxiliary_task=self.feature_extractor,
predict_from_pixels=hps['dyn_from_pixels'],
feat_dim=512)
self.agent = PpoOptimizer(
scope='ppo',
ob_space=self.ob_space,
ac_space=self.ac_space,
stochpol=self.policy,
use_news=hps['use_news'],
gamma=hps['gamma'],
lam=hps["lambda"],
nepochs=hps['nepochs'],
nminibatches=hps['nminibatches'],
lr=hps['lr'],
cliprange=0.1,
nsteps_per_seg=hps['nsteps_per_seg'],
nsegs_per_env=hps['nsegs_per_env'],
ent_coef=hps['ent_coeff'],
normrew=hps['norm_rew'],
normadv=hps['norm_adv'],
ext_coeff=hps['ext_coeff'],
int_coeff=hps['int_coeff'],
dynamics=self.dynamics,
load=hps['load'],
exp_name=hps['exp_name'],
)
self.agent.to_report['aux'] = tf.reduce_mean(
self.feature_extractor.loss)
self.agent.total_loss += self.agent.to_report['aux']
self.agent.to_report['dyn_loss'] = tf.reduce_mean(self.dynamics.loss)
self.agent.total_loss += self.agent.to_report['dyn_loss']
self.agent.to_report['feat_var'] = tf.reduce_mean(
tf.nn.moments(self.feature_extractor.features, [0, 1])[1])
def score(self):
self.agent.start_interaction(self.envs,
nlump=self.hps['nlumps'],
dynamics=self.dynamics)
from time import sleep
sleep(2)
episode_reward = 0
episode_rewards = []
total_episodes = 0
max_level = 0
max_levels = []
while True:
# info = self.agent.step()
# self.agent.rollout.collect_rollout()
obs, prevrews, news, infos = self.agent.rollout.env_get(0)
if prevrews is not None:
episode_reward += prevrews
if prevrews == 1:
max_level += 1
if news:
episode_rewards.append(episode_reward)
ave_reward = sum(episode_rewards) / len(episode_rewards)
total_episodes += 1
max_levels.append(max_level)
ave_level = sum(max_levels) / len(max_levels)
ave_level = np.around(ave_level, 2)
ave_reward = np.around(ave_reward, 2)
print('ep:', total_episodes, 'level:', max_level,
'ave_level:', ave_level, 'episode_reward:',
episode_reward, 'ave_reward', ave_reward)
episode_reward = 0
max_level = 0
if total_episodes >= 25:
break
# acs, vpreds, nlps = self.agent.rollout.policy.get_ac_value_nlp(obs)
# self.agent.rollout.env_step(0, acs)
acs, vpreds, nlps = self.policy.get_ac_value_nlp(obs)
self.agent.rollout.env_step(0, acs)
self.agent.rollout.step_count += 1
self.agent.stop_interaction()
