def start_interaction(self, env_fns, dynamics, nlump=2):
self.loss_names, self._losses = zip(*list(self.to_report.items()))
params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
if MPI.COMM_WORLD.Get_size() > 1:
trainer = MpiAdamOptimizer(learning_rate=self.ph_lr,
comm=MPI.COMM_WORLD)
else:
trainer = tf.train.AdamOptimizer(learning_rate=self.ph_lr)
gradsandvars = trainer.compute_gradients(self.total_loss, params)
self._train = trainer.apply_gradients(gradsandvars)
if MPI.COMM_WORLD.Get_rank() == 0:
getsess().run(
tf.variables_initializer(
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)))
bcast_tf_vars_from_root(
getsess(), tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES))
self.all_visited_rooms = []
self.all_scores = []
self.nenvs = nenvs = len(env_fns)
self.nlump = nlump
self.lump_stride = nenvs // self.nlump
self.envs = [
VecEnv(env_fns[l * self.lump_stride:(l + 1) * self.lump_stride],
spaces=[self.ob_space, self.ac_space])
for l in range(self.nlump)
]
self.rollout = Rollout(hps=self.hps,
ob_space=self.ob_space,
ac_space=self.ac_space,
nenvs=nenvs,
nsteps_per_seg=self.nsteps_per_seg,
nsegs_per_env=self.nsegs_per_env,
nlumps=self.nlump,
envs=self.envs,
policy=self.stochpol,
int_rew_coeff=self.int_coeff,
ext_rew_coeff=self.ext_coeff,
record_rollouts=self.use_recorder,
dynamics=dynamics)
self.buf_advs = np.zeros((nenvs, self.rollout.nsteps), np.float32)
self.buf_advs_int = np.zeros((nenvs, self.rollout.nsteps), np.float32)
self.buf_advs_ext = np.zeros((nenvs, self.rollout.nsteps), np.float32)
self.buf_rets = np.zeros((nenvs, self.rollout.nsteps), np.float32)
self.buf_rets_int = np.zeros((nenvs, self.rollout.nsteps), np.float32)
self.buf_rets_ext = np.zeros((nenvs, self.rollout.nsteps), np.float32)
if self.normrew:
self.rff = RewardForwardFilter(self.gamma)
self.rff_rms = RunningMeanStd()
self.step_count = 0
self.t_last_update = time.time()
self.t_start = time.time()
def start_interaction(self, env_fns, dynamics, nlump=2):
# 在开始与环境交互时定义变量和计算图, 初始化 rollout 类
self.loss_names, self._losses = zip(*list(self.to_report.items()))
# 定义损失、梯度和反向传播. 在训练时调用 sess.run(self._train) 进行迭代
params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
params_dvae = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="dvae_reward")
print("total params:", np.sum([np.prod(v.get_shape().as_list()) for v in params])) # 6629459
print("dvae params:", np.sum([np.prod(v.get_shape().as_list()) for v in params_dvae])) # 2726144
if MPI.COMM_WORLD.Get_size() > 1:
trainer = MpiAdamOptimizer(learning_rate=self.ph_lr, comm=MPI.COMM_WORLD)
else:
trainer = tf.train.AdamOptimizer(learning_rate=self.ph_lr)
gradsandvars = trainer.compute_gradients(self.total_loss, params)
self._train = trainer.apply_gradients(gradsandvars)
# add bai. 单独计算 DVAE 的梯度
gradsandvars_dvae = trainer.compute_gradients(self.dynamics_loss, params_dvae)
self._train_dvae = trainer.apply_gradients(gradsandvars_dvae)
if MPI.COMM_WORLD.Get_rank() == 0:
getsess().run(tf.variables_initializer(tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)))
bcast_tf_vars_from_root(getsess(), tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES))
self.all_visited_rooms = []
self.all_scores = []
self.nenvs = nenvs = len(env_fns) # 默认 128
self.nlump = nlump # 默认 1
self.lump_stride = nenvs // self.nlump # 128/1=128
self.envs = [
VecEnv(env_fns[l * self.lump_stride: (l + 1) * self.lump_stride], spaces=[self.ob_space, self.ac_space]) for
l in range(self.nlump)]
# 该类在 rollouts.py 中定义
self.rollout = Rollout(ob_space=self.ob_space, ac_space=self.ac_space, nenvs=nenvs,
nsteps_per_seg=self.nsteps_per_seg,
nsegs_per_env=self.nsegs_per_env, nlumps=self.nlump,
envs=self.envs,
policy=self.stochpol,
int_rew_coeff=self.int_coeff,
ext_rew_coeff=self.ext_coeff,
record_rollouts=self.use_recorder,
dynamics=dynamics)
# 环境数(线程数), 周期T
self.buf_advs = np.zeros((nenvs, self.rollout.nsteps), np.float32)
self.buf_rets = np.zeros((nenvs, self.rollout.nsteps), np.float32)
if self.normrew:
self.rff = RewardForwardFilter(self.gamma)
self.rff_rms = RunningMeanStd()
self.step_count = 0
self.t_last_update = time.time()
self.t_start = time.time()
def start_interaction(self, env_fns, dynamics, nlump=2):
self.loss_names, self._losses = zip(*list(self.to_report.items()))
self.global_step = tf.Variable(0, trainable=False)
params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
if MPI.COMM_WORLD.Get_size() > 1:
if self.agent_num is None:
trainer = MpiAdamOptimizer(learning_rate=self.ph_lr,
comm=MPI.COMM_WORLD)
else:
if self.agent_num is None:
if self.optim == 'adam':
trainer = tf.train.AdamOptimizer(learning_rate=self.ph_lr)
elif self.optim == 'sgd':
print("using sgd")
print("________________________")
if self.decay:
self.decay_lr = tf.train.exponential_decay(
self.ph_lr,
self.global_step,
2500,
.96,
staircase=True)
trainer = tf.train.GradientDescentOptimizer(
learning_rate=self.decay_lr)
else:
trainer = tf.train.GradientDescentOptimizer(
learning_rate=self.ph_lr)
elif self.optim == 'momentum':
print('using momentum')
print('________________________')
trainer = tf.train.MomentumOptimizer(
learning_rate=self.ph_lr, momentum=0.9)
if self.agent_num is None:
gradsandvars = trainer.compute_gradients(self.total_loss, params)
l2_norm = lambda t: tf.sqrt(tf.reduce_sum(tf.pow(t, 2)))
if self.log_grads:
for grad, var in gradsandvars:
tf.summary.histogram(var.name + '/gradient', l2_norm(grad))
tf.summary.histogram(var.name + '/value', l2_norm(var))
grad_mean = tf.reduce_mean(tf.abs(grad))
tf.summary.scalar(var.name + '/grad_mean', grad_mean)
if self.decay:
tf.summary.scalar('decay_lr', self.decay_lr)
self._summary = tf.summary.merge_all()
tf.add_to_collection("summary_op", self._summary)
if self.grad_clip > 0:
grads, gradvars = zip(*gradsandvars)
grads, _ = tf.clip_by_global_norm(grads, self.grad_clip)
gradsandvars = list(zip(grads, gradvars))
self._train = trainer.apply_gradients(gradsandvars,
global_step=self.global_step)
self._updates = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
self._train = tf.group(self._train, self._updates)
tf.add_to_collection("train_op", self._train)
else:
self._train = tf.get_collection("train_op")[0]
if self.log_grads:
self._summary = tf.get_collection("summary_op")[0]
if MPI.COMM_WORLD.Get_rank() == 0:
getsess().run(
tf.variables_initializer(
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)))
bcast_tf_vars_from_root(
getsess(), tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES))
self.all_visited_rooms = []
self.all_scores = []
self.nenvs = nenvs = len(env_fns)
self.nlump = nlump
self.lump_stride = nenvs // self.nlump
self.envs = [
VecEnv(env_fns[l * self.lump_stride:(l + 1) * self.lump_stride],
spaces=[self.env_ob_space, self.ac_space])
for l in range(self.nlump)
]
self.rollout = Rollout(ob_space=self.ob_space,
ac_space=self.ac_space,
nenvs=nenvs,
nsteps_per_seg=self.nsteps_per_seg,
nsegs_per_env=self.nsegs_per_env,
nlumps=self.nlump,
envs=self.envs,
policy=self.stochpol,
int_rew_coeff=self.int_coeff,
ext_rew_coeff=self.ext_coeff,
record_rollouts=self.use_recorder,
dynamics=dynamics,
exp_name=self.exp_name,
env_name=self.env_name,
video_log_freq=self.video_log_freq,
model_save_freq=self.model_save_freq,
use_apples=self.use_apples,
multi_envs=self.multi_envs,
lstm=self.lstm,
lstm1_size=self.lstm1_size,
lstm2_size=self.lstm2_size,
depth_pred=self.depth_pred,
early_stop=self.early_stop,
aux_input=self.aux_input)
self.buf_advs = np.zeros((nenvs, self.rollout.nsteps), np.float32)
self.buf_rets = np.zeros((nenvs, self.rollout.nsteps), np.float32)
if self.normrew:
self.rff = RewardForwardFilter(self.gamma)
self.rff_rms = RunningMeanStd()
self.step_count = 0
self.t_last_update = time.time()
self.t_start = time.time()
def start_interaction(self, env_fns, dynamics, nlump=2):
self.loss_names, self._losses = zip(*list(self.to_report.items()))
params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
if MPI.COMM_WORLD.Get_size() > 1:
trainer = MpiAdamOptimizer(learning_rate=self.ph_lr,
comm=MPI.COMM_WORLD)
else:
trainer = tf.train.AdamOptimizer(learning_rate=self.ph_lr)
#gvs = trainer.compute_gradients(self.total_loss, params)
#self.gshape = gs
#gs = [g for (g,v) in gvs]
#self.normg = tf.linalg.global_norm(gs)
#new_g = [tf.clip_by_norm(g,10.0) for g in gs i]
#self.nnormg = tf.linalg.global_norm(new_g)
def ClipIfNotNone(grad):
return tf.clip_by_value(grad, -25.0,
25.0) if grad is not None else grad
gradsandvars = trainer.compute_gradients(self.total_loss, params)
#gs = [g for (g,v) in gradsandvars]
#new_g = [tf.clip_by_norm(g,10.0) for g in gs if g is not None]
gradsandvars = [(ClipIfNotNone(g), v) for g, v in gradsandvars]
#new_g = [g for (g,v) in gradsandvars]
#self.nnormg = tf.linalg.global_norm(new_g)
#gradsandvars = [(ClipIfNotNone(grad), var) for grad, var in gradsandvars]
self._train = trainer.apply_gradients(gradsandvars)
if MPI.COMM_WORLD.Get_rank() == 0:
getsess().run(
tf.variables_initializer(
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)))
bcast_tf_vars_from_root(
getsess(), tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES))
self.all_visited_rooms = []
self.all_scores = []
self.nenvs = nenvs = len(env_fns)
print('-------NENVS-------', self.nenvs)
self.nlump = nlump
print('----------NLUMPS-------', self.nlump)
self.lump_stride = nenvs // self.nlump
print('-------LSTRIDE----', self.lump_stride)
print('--------OBS SPACE ---------', self.ob_space)
print('-------------AC SPACE-----', self.ac_space)
#assert 1==2
print('-----BEFORE VEC ENV------')
self.envs = [
VecEnv(env_fns[l * self.lump_stride:(l + 1) * self.lump_stride],
spaces=[self.ob_space, self.ac_space])
for l in range(self.nlump)
]
print('-----AFTER VEC ENV------')
self.rollout = Rollout(ob_space=self.ob_space,
ac_space=self.ac_space,
nenvs=nenvs,
nsteps_per_seg=self.nsteps_per_seg,
nsegs_per_env=self.nsegs_per_env,
nlumps=self.nlump,
envs=self.envs,
policy=self.stochpol,
int_rew_coeff=self.int_coeff,
ext_rew_coeff=self.ext_coeff,
record_rollouts=self.use_recorder,
dynamics=dynamics,
exp_name=self.exp_name,
env_name=self.env_name,
to_eval=self.to_eval)
self.buf_advs = np.zeros((nenvs, self.rollout.nsteps), np.float32)
self.buf_rets = np.zeros((nenvs, self.rollout.nsteps), np.float32)
if self.normrew:
self.rff = RewardForwardFilter(self.gamma)
self.rff_rms = RunningMeanStd()
self.step_count = 0
self.t_last_update = time.time()
self.t_start = time.time()
self.saver = tf.train.Saver(max_to_keep=5)