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'])
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 __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.ob_mean, self.ob_std, self.ob_space, self.ac_space = random_agent_ob_mean_std(
None, hps['env'], nsteps=1, load=True)
self.env = self.make_env(258)
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.agents = [
# self.create_agent('presub095', hps),
self.create_agent('presub089', hps),
# self.create_agent('presub088', hps),
# self.create_agent('presub087', hps),
# self.create_agent('presub047', hps),
# self.create_agent('presub018', hps),
# self.create_agent('presub001', hps),
# self.create_agent('presub002', hps),
# self.create_agent('presub004', hps),
# self.create_agent('presub005', hps),
# self.create_agent('presub015', hps),
# self.create_agent('presub016', hps),
# self.create_agent('presub017', hps),
# self.create_agent('presub019', hps),
# self.create_agent('presub020', hps),
# self.create_agent('presub021', hps),
]
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.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
)
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 __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.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=torch.nn.LeakyReLU)
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,
# if we use VAE, 'features_shared_with_policy' should be set to False,
# because the shape of output_features of VAE.get_features is feat_dims * 2, including means and stds,
# but the shape of out_features of policy.get_features is feat_dims,
# only means is used as features exposed to dynamics
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)
def __init__(self, make_env, num_timesteps, envs_per_process):
self.make_env = make_env
self.envs_per_process = envs_per_process
self.num_timesteps = num_timesteps
self._set_env_vars()
self.policy = CnnPolicy(scope='cnn_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 = InverseDynamics(policy=self.policy,
feat_dim=512,
layernormalize=0)
self.dynamics = Dynamics(auxiliary_task=self.feature_extractor,
mode=MODE,
feat_dim=512)
self.agent = PpoOptimizer(
scope='ppo',
ob_space=self.ob_space,
ac_space=self.ac_space,
policy=self.policy,
use_news=0,
gamma=.99,
lam=.98, #TODO Change this for potentially vastly different results
nepochs=3,
nminibatches=16,
lr=1e-4,
cliprange=.1, #TODO Change this as well
nsteps_per_seg=256,
nsegs_per_env=1,
ent_coeff=.001,
normrew=1,
normadv=1,
ext_coeff=0.,
int_coeff=1.,
dynamics=self.dynamics)
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])
# 一个随机智能体与环境交互, 计算得到的观测的均值和标准差.
from utils import random_agent_ob_mean_std
ob_mean, ob_std = random_agent_ob_mean_std(env)
print("obs mean:", ob_mean.shape, np.max(ob_mean), np.min(ob_mean))
print("obs std:", ob_std.shape, np.max(ob_std), np.min(ob_std))
# 初始化环境
envs = [partial(make_env, i) for i in range(5)]
# CNN policy
print("Init Policy.")
policy = CnnPolicy(scope='pol',
ob_space=ob_space,
ac_space=ac_space,
hidsize=512,
feat_dim=512,
ob_mean=ob_mean,
ob_std=ob_std,
layernormalize=False,
nl=tf.nn.leaky_relu)
print("Init Feature Extractor.")
feature_extractor = FeatureExtractor(policy=policy,
features_shared_with_policy=False,
feat_dim=512,
layernormalize=False)
# agent 损失: 包括 actor,critic,entropy 损失; 先在加上 feature 学习时包含的损失
print(feature_extractor.loss.shape)
# feature_extractor.features.shape=(None,None,512)
mean_std = tf.nn.moments(feature_extractor.features, [0, 1])
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.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,
ama=hps["ama"],
uncertainty_penalty=hps["uncertainty_penalty"],
clip_ama=hps["clip_ama"],
clip_val=hps["clip_val"],
reward_scaling=hps["reward_scaling"],
abs_ama=hps["abs_ama"])
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,
args=hps,
)
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[0])
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 __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.policy = CnnPolicy(scope='pol',
ob_space=self.ob_space,
ac_space=self.ac_space,
hidsize=512,
feat_dim=hps['feat_dim'],
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=hps['feat_dim'],
layernormalize=hps['layernorm'])
self.intrinsic_model = IntrinsicModel if hps[
'feat_learning'] != 'pix2pix' else UNet
self.intrinsic_model = self.intrinsic_model(
auxiliary_task=self.feature_extractor,
predict_from_pixels=hps['dyn_from_pixels'],
feature_space=hps['feature_space'],
nsteps_per_seg=hps['nsteps_per_seg'],
feat_dim=hps['feat_dim'],
naudio_samples=hps['naudio_samples'],
train_discriminator=hps['train_discriminator'],
discriminator_weighted=hps['discriminator_weighted'],
noise_multiplier=hps['noise_multiplier'],
concat=hps['concat'],
log_dir=logger.get_dir(),
make_video=hps['checkpoint_path'] != '')
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'],
feature_space=hps['feature_space'],
intrinsic_model=self.intrinsic_model,
log_dir=logger.get_dir(),
checkpoint_path=hps['checkpoint_path'])
self.agent.to_report['aux'] = tf.reduce_mean(
self.feature_extractor.loss)
self.agent.total_loss += self.agent.to_report['aux']
if hps['feature_space'] == 'joint':
self.agent.to_report['dyn_visual_loss'] = tf.reduce_mean(
self.intrinsic_model.visual_loss)
self.agent.to_report['dyn_audio_loss'] = tf.reduce_mean(
self.intrinsic_model.audio_loss)
self.agent.to_report['discrim_train_loss'] = tf.reduce_mean(
self.intrinsic_model.discrim_train_loss)
self.agent.to_report['intrinsic_model_loss'] = tf.reduce_mean(
self.intrinsic_model.loss)
elif hps['train_discriminator']:
self.agent.to_report['intrinsic_model_loss'] = tf.reduce_mean(
self.intrinsic_model.discrim_train_loss)
else:
self.agent.to_report['intrinsic_model_loss'] = tf.reduce_mean(
self.intrinsic_model.loss)
self.agent.total_loss += self.agent.to_report['intrinsic_model_loss']
self.agent.to_report['feat_var'] = tf.reduce_mean(
tf.nn.moments(self.feature_extractor.features, [0, 1])[1])
def main(args):
# mpi communicator.
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
# seed.
workerseed = args.seed + 10000 * comm.Get_rank() if args.seed is not None else None
if workerseed is not None:
tc.manual_seed(workerseed % 2 ** 32)
np.random.seed(workerseed % 2 ** 32)
random.seed(workerseed % 2 ** 32)
# logger.
if rank == 0:
logger.configure()
else:
logger.configure(format_strs=[])
# env.
env = make_atari(args.env_name)
env.seed(workerseed)
env = Monitor(env, logger.get_dir() and
os.path.join(logger.get_dir(), str(rank)))
print(f"frame_stacking: {args.frame_stacking}")
env = wrap_deepmind(env, frame_stack=args.frame_stacking,
clip_rewards=(args.mode =='train'),
episode_life=(args.mode =='train')) # See Mnih et al., 2015 -> Methods -> Training Details.
env.seed(workerseed)
# agent.
agent = CnnPolicy(
img_channels=env.observation_space.shape[-1],
num_actions=env.action_space.n,
kind=args.model_type)
# optimizer and scheduler.
max_grad_steps = args.optim_epochs * args.env_steps // (comm.Get_size() * args.optim_batchsize)
optimizer = tc.optim.Adam(agent.parameters(), lr=args.optim_stepsize, eps=1e-5)
scheduler = tc.optim.lr_scheduler.OneCycleLR(
optimizer=optimizer, max_lr=args.optim_stepsize, total_steps=max_grad_steps,
pct_start=0.0, anneal_strategy='linear', cycle_momentum=False,
div_factor=1.0)
# checkpoint.
if rank == 0:
try:
state_dict = tc.load(os.path.join(args.checkpoint_dir, args.model_name, 'model.pth'))
agent.load_state_dict(state_dict)
print(f"Continuing from checkpoint found at {os.path.join(args.checkpoint_dir, args.model_name, 'model.pth')}")
except FileNotFoundError:
print("Bad checkpoint or none on process 0. Continuing from scratch.")
# sync.
with tc.no_grad():
for p in agent.parameters():
p_data = p.data.numpy()
comm.Bcast(p_data, root=0)
p.data.copy_(tc.tensor(p_data).float())
# operations.
if args.mode == 'train':
learn(env=env, agent=agent, optimizer=optimizer, scheduler=scheduler, comm=comm,
timesteps_per_actorbatch=args.timesteps_per_actorbatch, max_timesteps=args.env_steps,
optim_epochs=args.optim_epochs, optim_batchsize=args.optim_batchsize,
gamma=args.gamma, lam=args.lam, clip_param=args.epsilon, entcoeff=args.ent_coef,
checkpoint_dir=args.checkpoint_dir, model_name=args.model_name)
env.close()
elif args.mode == 'play':
if comm.Get_rank() == 0:
play(env=env, agent=agent, args=args)
env.close()
elif args.mode == 'movie':
if comm.Get_rank() == 0:
movie(env=env, agent=agent, args=args)
env.close()
else:
raise NotImplementedError("Mode of operation not supported!")
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)
# 在建立环境模型之前, 先初始特征提取器. 定义在 auxiliary_task.py 中. 其中 pix2pix 相当于没有提取特征.
self.feature_extractor = {
"none": FeatureExtractor, # 默认是none
"idf": InverseDynamics,
"vaesph": partial(VAE, spherical_obs=True),
"vaenonsph": partial(VAE, spherical_obs=False),
"pix2pix": JustPixels
}[hps['feat_learning']] # 通过hps参数选择一个特征提取器
self.feature_extractor = self.feature_extractor(
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, # dynamic 对象
nepochs_dvae=hps["nepochs_dvae"] # 额外训练 dynamic 的次数
)
# 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
self.agent.to_report['dyn_loss'] = tf.reduce_mean(self.dynamics.loss)
self.agent.total_loss += self.agent.to_report['dyn_loss']
# add bai. 单独记录DAVE损失，可能要多次训练DAVE
self.agent.dynamics_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 __init__(self, make_env, hps, num_timesteps, envs_per_process, exp_name=None, env_name=None, policy=None, feat_ext=None, dyn=None, agent_num=None, restore_name=None):
self.make_env = make_env
self.hps = hps
self.envs_per_process = envs_per_process
self.depth_pred = hps['depth_pred']
self.aux_input = hps['aux_input']
self.num_timesteps = num_timesteps
self._set_env_vars()
if exp_name:
self.exp_name = exp_name
else:
self.exp_name = hps['exp_name']
if env_name:
self.env_name = env_name
else:
self.env_name = hps['env']
if policy is None:
if hps['lstm']:
self.policy = LSTMPolicy(
scope='pol',
ob_space=self.ob_space,
ac_space=self.ac_space,
hidsize=512,
batchsize=hps['envs_per_process'],
feat_dim=512,
ob_mean=self.ob_mean,
ob_std=self.ob_std,
lstm1_size=hps['lstm1_size'],
lstm2_size=hps['lstm2_size'],
layernormalize=False,
nl=tf.nn.leaky_relu,
depth_pred=hps['depth_pred'],
aux_input=hps['aux_input'],
)
else:
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
)
else:
self.policy = policy
self.policy.restore()
if feat_ext:
self.feature_extractor = feat_ext
self.feature_extractor.restore()
else:
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=hps['feat_share'],
feat_dim=hps['dyn_feat_dim'],
layernormalize=hps['layernorm'])
if dyn:
self.dynamics = dyn
self.dynamics.restore()
else:
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=hps['dyn_feat_dim'])
self.agent = PpoOptimizer(
hps=hps,
scope='ppo',
ob_space=self.ob_space,
env_ob_space=self.env_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,
exp_name=self.exp_name,
env_name=self.env_name,
video_log_freq=hps['video_log_freq'],
model_save_freq=hps['model_save_freq'],
use_apples=hps['use_apples'],
agent_num=agent_num,
restore_name=restore_name,
multi_envs=hps['multi_train_envs'],
lstm=hps['lstm'],
lstm1_size=hps['lstm1_size'],
lstm2_size=hps['lstm2_size'],
depth_pred=hps['depth_pred'],
aux_input=hps['aux_input'],
beta_d=hps['beta'],
early_stop=hps['early_stop'],
optim=hps['optim'],
decay=hps['decay'],
grad_clip=hps['grad_clip'],
log_grads=hps['log_grads'],
logdir=hps['logdir']
)
self.agent.to_report['aux'] = tf.reduce_mean(self.feature_extractor.loss)
self.agent.to_report['dyn_loss'] = tf.reduce_mean(self.dynamics.loss)
self.agent.to_report['feat_var'] = tf.reduce_mean(tf.nn.moments(self.feature_extractor.features, [0,1])[1])
if hps['curiosity']:
#self.agent.to_report['aux'] = tf.reduce_mean(self.feature_extractor.loss)
self.agent.total_loss += hps['aux_loss_coeff']*self.agent.to_report['aux']
#self.agent.to_report['dyn_loss'] = tf.reduce_mean(self.dynamics.loss)
self.agent.total_loss += hps['dyn_loss_coeff']*self.agent.to_report['dyn_loss']
class Trainer(object):
from baselines import logger
def __init__(self, make_env, hps, num_timesteps, envs_per_process, exp_name=None, env_name=None, policy=None, feat_ext=None, dyn=None, agent_num=None, restore_name=None):
self.make_env = make_env
self.hps = hps
self.envs_per_process = envs_per_process
self.depth_pred = hps['depth_pred']
self.aux_input = hps['aux_input']
self.num_timesteps = num_timesteps
self._set_env_vars()
if exp_name:
self.exp_name = exp_name
else:
self.exp_name = hps['exp_name']
if env_name:
self.env_name = env_name
else:
self.env_name = hps['env']
if policy is None:
if hps['lstm']:
self.policy = LSTMPolicy(
scope='pol',
ob_space=self.ob_space,
ac_space=self.ac_space,
hidsize=512,
batchsize=hps['envs_per_process'],
feat_dim=512,
ob_mean=self.ob_mean,
ob_std=self.ob_std,
lstm1_size=hps['lstm1_size'],
lstm2_size=hps['lstm2_size'],
layernormalize=False,
nl=tf.nn.leaky_relu,
depth_pred=hps['depth_pred'],
aux_input=hps['aux_input'],
)
else:
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
)
else:
self.policy = policy
self.policy.restore()
if feat_ext:
self.feature_extractor = feat_ext
self.feature_extractor.restore()
else:
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=hps['feat_share'],
feat_dim=hps['dyn_feat_dim'],
layernormalize=hps['layernorm'])
if dyn:
self.dynamics = dyn
self.dynamics.restore()
else:
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=hps['dyn_feat_dim'])
self.agent = PpoOptimizer(
hps=hps,
scope='ppo',
ob_space=self.ob_space,
env_ob_space=self.env_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,
exp_name=self.exp_name,
env_name=self.env_name,
video_log_freq=hps['video_log_freq'],
model_save_freq=hps['model_save_freq'],
use_apples=hps['use_apples'],
agent_num=agent_num,
restore_name=restore_name,
multi_envs=hps['multi_train_envs'],
lstm=hps['lstm'],
lstm1_size=hps['lstm1_size'],
lstm2_size=hps['lstm2_size'],
depth_pred=hps['depth_pred'],
aux_input=hps['aux_input'],
beta_d=hps['beta'],
early_stop=hps['early_stop'],
optim=hps['optim'],
decay=hps['decay'],
grad_clip=hps['grad_clip'],
log_grads=hps['log_grads'],
logdir=hps['logdir']
)
self.agent.to_report['aux'] = tf.reduce_mean(self.feature_extractor.loss)
self.agent.to_report['dyn_loss'] = tf.reduce_mean(self.dynamics.loss)
self.agent.to_report['feat_var'] = tf.reduce_mean(tf.nn.moments(self.feature_extractor.features, [0,1])[1])
if hps['curiosity']:
#self.agent.to_report['aux'] = tf.reduce_mean(self.feature_extractor.loss)
self.agent.total_loss += hps['aux_loss_coeff']*self.agent.to_report['aux']
#self.agent.to_report['dyn_loss'] = tf.reduce_mean(self.dynamics.loss)
self.agent.total_loss += hps['dyn_loss_coeff']*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 _set_env_vars(self):
import numpy as np
env = self.make_env(0, add_monitor=False)
self.ob_space, self.ac_space = env.observation_space, env.action_space
self.env_ob_space = env.observation_space
if self.depth_pred:
self.ob_space = gym.spaces.Box(0, 255, shape=(84,84,3), dtype=np.uint8)
self.ob_mean, self.ob_std = random_agent_ob_mean_std(env, depth_pred=self.hps['depth_pred'])
del env
self.envs = [functools.partial(self.make_env, i) for i in range(self.envs_per_process)]
def train(self, saver, sess, restore=False):
from baselines import logger
self.agent.start_interaction(self.envs, nlump=self.hps['nlumps'], dynamics=self.dynamics)
if restore:
print("Restoring model for training")
saver.restore(sess, "models/" + self.hps['restore_model'] + ".ckpt")
print("Loaded model", self.hps['restore_model'])
write_meta_graph = False
while True:
info = self.agent.step()
if info['update']:
if info['update']['recent_best_ext_ret'] is None:
info['update']['recent_best_ext_ret'] = 0
wandb.log(info['update'])
logger.logkvs(info['update'])
logger.dumpkvs()
if self.agent.rollout.stats['tcount'] > self.num_timesteps:
break
if self.hps['tune_env']:
filename = "models/" + self.hps['restore_model'] + "_tune_on_" + self.hps['tune_env'] + "_final.ckpt"
else:
filename = "models/" + self.hps['exp_name'] + "_final.ckpt"
saver.save(sess, filename, write_meta_graph=False)
self.policy.save_model(self.hps['exp_name'], 'final')
self.agent.stop_interaction()
class Trainer(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.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'],
pred_discount=hps['pred_discount'],
num_preds=hps['num_preds'],
feat_dim=512)
''' Setting dynamics object in policy for feature extraction'''
self.policy.set_dynamics(self.dynamics)
self.dynamics.set_loss()
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)
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.loss1 +
self.dynamics.loss2)
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 _set_env_vars(self):
env = self.make_env(0, add_monitor=True)
self.ob_space, self.ac_space = env.observation_space, env.action_space
self.ob_mean, self.ob_std = random_agent_ob_mean_std(env)
del env
self.envs = [
functools.partial(self.make_env, i)
for i in range(self.envs_per_process)
]
def train(self):
self.agent.start_interaction(self.envs,
nlump=self.hps['nlumps'],
dynamics=self.dynamics)
while True:
info = self.agent.step()
if info['update']:
logger.logkvs(info['update'])
logger.dumpkvs()
if self.agent.rollout.stats['tcount'] > self.num_timesteps:
break
self.agent.stop_interaction()
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.ob_mean, self.ob_std, self.ob_space, self.ac_space = random_agent_ob_mean_std(
None, hps['env'], nsteps=1, load=True)
self.env = self.make_env(258)
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.agents = [
# self.create_agent('presub095', hps),
self.create_agent('presub089', hps),
# self.create_agent('presub088', hps),
# self.create_agent('presub087', hps),
# self.create_agent('presub047', hps),
# self.create_agent('presub018', hps),
# self.create_agent('presub001', hps),
# self.create_agent('presub002', hps),
# self.create_agent('presub004', hps),
# self.create_agent('presub005', hps),
# self.create_agent('presub015', hps),
# self.create_agent('presub016', hps),
# self.create_agent('presub017', hps),
# self.create_agent('presub019', hps),
# self.create_agent('presub020', hps),
# self.create_agent('presub021', hps),
]
def create_agent(self, exp_name, hps):
# graph = tf.Graph()
# graph.as_default()
agent = PpoOptimizer(
scope=exp_name,
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=exp_name,
)
agent.to_report['aux'] = tf.reduce_mean(self.feature_extractor.loss)
agent.total_loss += agent.to_report['aux']
agent.to_report['dyn_loss'] = tf.reduce_mean(self.dynamics.loss)
agent.total_loss += agent.to_report['dyn_loss']
agent.to_report['feat_var'] = tf.reduce_mean(
tf.nn.moments(self.feature_extractor.features, [0, 1])[1])
# agent.graph = graph
# tf.reset_default_graph()
return agent
def score(self):
episode_reward = 0
episode_rewards = []
total_episodes = 0
samples = 0
obs = np.empty((len(self.agents) + samples, 1, *self.ob_space.shape),
np.float32)
obs[0] = self.env.reset()
max_level = 0
max_levels = []
for agent in self.agents:
agent.no_mpi_start_interaction([self.env],
nlump=self.hps['nlumps'],
dynamics=self.dynamics)
# if is_grading(self.env):
# while not done_grading(self.env):
# # run_episode(env)
# done = False
# episode_reward = 0.0
# while not done:
# action = env.action_space.sample()
# obs, reward, done, info = env.step(action)
# episode_reward += reward
# self.env.reset()
# return
while True:
# aa = obs.reshape([len(obs) * 1, 1, *self.ob_space.shape])
for i in range(len(self.agents) - 1):
obs[1 + i] = obs[0]
for i in range(samples):
mu, sigma = 0, 0.1
noise = np.random.normal(mu, sigma, obs[0].shape)
obs[len(self.agents) + i] = obs[0] + noise
# obs[1] = np.copy(obs[0])
# obs[1] = cv2.randn(obs[1],(128),(9))
action_scores, acs, vpreds, nlps = self.policy.inference_get_ac_value_nlp(
obs)
max_actions = np.unravel_index(action_scores.argmax(),
action_scores.shape)
max_action = max_actions[1]
max_v = vpreds.argmax()
max_npl = nlps.argmax()
min_npl = nlps.argmin()
action = acs[0] # default
# action = int(max_action) # based on highest scoring action
# action = int(acs[max_v]) # based on highest value
# action = int(acs[min_npl]) # based on min npl
# action = action_scores[min_npl].argmax()
ob, reward, done, _ = self.env.step(action)
obs[0] = ob
episode_reward += reward
if reward == 1:
max_level += 1
if done:
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)
print('ep:', total_episodes, 'level:', max_level, 'ave_level:',
round(ave_level, 2), 'episode_reward:', episode_reward,
'ave_reward', round(ave_reward, 2))
episode_reward = 0
max_level = 0
if is_grading(self.env):
if done_grading(self.env):
break
elif total_episodes >= 25:
break
obs[0] = self.env.reset()
self.env.close()
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()
