def load(path, num_cpu=16):
with open(path, "rb") as f:
model_data, act_params = dill.load(f)
act = build_graph.build_act(**act_params)
sess = U.make_session(num_cpu=num_cpu)
sess.__enter__()
with tempfile.TemporaryDirectory() as td:
arc_path = os.path.join(td, "packed.zip")
with open(arc_path, "wb") as f:
f.write(model_data)
zipfile.ZipFile(arc_path, 'r', zipfile.ZIP_DEFLATED).extractall(td)
U.load_state(os.path.join(td, "model"))
return ActWrapper(act, act_params)
def load(path):
with open(path, "rb") as f:
model_data, act_params = cloudpickle.load(f)
act = build_act(**act_params)
sess = tf.Session()
sess.__enter__()
with tempfile.TemporaryDirectory() as td:
arc_path = os.path.join(td, "packed.zip")
with open(arc_path, "wb") as f:
f.write(model_data)
zipfile.ZipFile(arc_path, 'r', zipfile.ZIP_DEFLATED).extractall(td)
load_state(os.path.join(td, "model"))
return ActWrapper(act, act_params)
def build_train(make_obs_ph, q_func, num_actions, optimizer, grad_norm_clipping=None, gamma=1.0,
double_q=True, scope="deepq", reuse=None, param_noise=False, param_noise_filter_func=None):
"""Creates the train function:
Parameters
----------
make_obs_ph: str -> tf.placeholder or TfInput
a function that takes a name and creates a placeholder of input with that name
q_func: (tf.Variable, int, str, bool) -> tf.Variable
the model that takes the following inputs:
observation_in: object
the output of observation placeholder
num_actions: int
number of actions
scope: str
reuse: bool
should be passed to outer variable scope
and returns a tensor of shape (batch_size, num_actions) with values of every action.
num_actions: int
number of actions
reuse: bool
whether or not to reuse the graph variables
optimizer: tf.train.Optimizer
optimizer to use for the Q-learning objective.
grad_norm_clipping: float or None
clip gradient norms to this value. If None no clipping is performed.
gamma: float
discount rate.
double_q: bool
if true will use Double Q Learning (https://arxiv.org/abs/1509.06461).
In general it is a good idea to keep it enabled.
scope: str or VariableScope
optional scope for variable_scope.
reuse: bool or None
whether or not the variables should be reused. To be able to reuse the scope must be given.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
param_noise_filter_func: tf.Variable -> bool
function that decides whether or not a variable should be perturbed. Only applicable
if param_noise is True. If set to None, default_param_noise_filter is used by default.
Returns
-------
act: (tf.Variable, bool, float) -> tf.Variable
function to select and action given observation.
` See the top of the file for details.
train: (object, np.array, np.array, object, np.array, np.array) -> np.array
optimize the error in Bellman's equation.
` See the top of the file for details.
update_target: () -> ()
copy the parameters from optimized Q function to the target Q function.
` See the top of the file for details.
debug: {str: function}
a bunch of functions to print debug data like q_values.
"""
if param_noise:
act_f = build_act_with_param_noise(make_obs_ph, q_func, num_actions, scope=scope, reuse=reuse,
param_noise_filter_func=param_noise_filter_func)
else:
act_f = build_act(make_obs_ph, q_func, num_actions, scope=scope, reuse=reuse)
with tf.variable_scope(scope, reuse=reuse):
multi_step_num = 3 # multi step return 10, 5
gamma = 0.7 # 折扣率
# set up placeholders
obs_t_input = make_obs_ph("obs_t")
act_t_ph = tf.placeholder(tf.int32, [None], name="action")
rew_t_ph = tf.placeholder(tf.float32, [None], name="reward") # 如果要使用长期回报,这里需要一个数组
obs_tp1_input = make_obs_ph("obs_tp1")
done_mask_ph = tf.placeholder(tf.float32, [None], name="done")
importance_weights_ph = tf.placeholder(tf.float32, [None], name="weight")
# 创建Q network 与 target Q network , 返回所有action 的q值(q_func()) , q_t是一个列表
# q network evaluation
q_t = q_func(obs_t_input.get(), num_actions, scope="q_func", reuse=True) # reuse parameters from act
q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=tf.get_variable_scope().name + "/q_func")
q_tp1 = q_func(obs_tp1_input.get(), num_actions, scope="target_q_func")
target_q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=tf.get_variable_scope().name + "/target_q_func")
# q scores for actions which we know were selected in the given state.
q_t_selected = tf.reduce_sum(q_t * tf.one_hot(act_t_ph, num_actions), 1)
# compute estimate of best possible value starting from state at t + 1
if double_q:
q_tp1_using_online_net = q_func(obs_tp1_input.get(), num_actions, scope="q_func", reuse=True)
q_tp1_best_using_online_net = tf.argmax(q_tp1_using_online_net, 1)
q_tp1_best = tf.reduce_sum(q_tp1 * tf.one_hot(q_tp1_best_using_online_net, num_actions), 1)
else:
q_tp1_best = tf.reduce_max(q_tp1, 1)
q_tp1_best_masked = (1.0 - done_mask_ph) * q_tp1_best
# compute RHS of bellman equation ,TD目标
# q_t_selected_target = rew_t_ph + gamma * q_tp1_best_masked
q_t_selected_target = rew_t_ph + (gamma**multi_step_num) * q_tp1_best_masked # multi step return
# compute the error (potentially clipped)
td_error = q_t_selected - tf.stop_gradient(q_t_selected_target)
errors = U.huber_loss(td_error)
weighted_error = tf.reduce_mean(importance_weights_ph * errors)
# # start cpu
# with tf.device('/cpu:0'):
# # compute optimization op (potentially with gradient clipping)
# if grad_norm_clipping is not None:
# gradients = optimizer.compute_gradients(weighted_error, var_list=q_func_vars)
# for i, (grad, var) in enumerate(gradients):
# if grad is not None:
# gradients[i] = (tf.clip_by_norm(grad, grad_norm_clipping), var)
# optimize_expr = optimizer.apply_gradients(gradients)
# else:
# optimize_expr = optimizer.minimize(weighted_error, var_list=q_func_vars)
# # end cpu
# compute optimization op (potentially with gradient clipping)
if grad_norm_clipping is not None:
gradients = optimizer.compute_gradients(weighted_error, var_list=q_func_vars)
for i, (grad, var) in enumerate(gradients):
if grad is not None:
gradients[i] = (tf.clip_by_norm(grad, grad_norm_clipping), var)
optimize_expr = optimizer.apply_gradients(gradients)
else:
optimize_expr = optimizer.minimize(weighted_error, var_list=q_func_vars)
# update_target_fn will be called periodically to copy Q network to target Q network
# sorted() 不会改变原来的可迭代对象
update_target_expr = []
for var, var_target in zip(sorted(q_func_vars, key=lambda v: v.name), # 利用q_func_vars.name 进行排序,
sorted(target_q_func_vars, key=lambda v: v.name)):
# print(var) # 这里var\var_target 就是一个tensor
# print(var_target)
update_target_expr.append(var_target.assign(var))
# print(update_target_expr) # 大概就是一系列Assign操作
update_target_expr = tf.group(*update_target_expr) # tf.group()将语句变为操作？
# 因为是单进程写,多进程读,所以将两种操作分别应用于不同内存区域上,降低lock竞争,仍然有一些不够合理的地方
# 初始化actor的q网络
def init_actor_qfunc(sess, net_list):
# 需要tf.variable_scope(scope, reuse=reuse): 因而写在这里
# 或使用tf.get_default_session()(不可用上下文管理器)
with sess.as_default():
# net_list_lock.acquire()
# 清空list
i = len(net_list)
while i > 0:
i -= 1
del net_list[i]
for var_actor in q_func_vars: # 整体顺序是否正确,有待进一步观察
net_list.append(var_actor.eval(session=sess)) # list形式
# for var_actor in q_func_vars: # net_list 长度为q_func_vars两倍
# net_list.append(var_actor.eval(session=sess))
gc.collect() # 释放内存, python3.5 应该不需要
# net_list_lock.release() # 释放锁
len_q_func = len(q_func_vars)
# 更新actor的q网络
def update_actor_qfunc(sess, net_list, net_list_lock):
with sess.as_default():
net_list_lock.acquire()
for i_tensor in range(len_q_func):
net_list[i_tensor] = q_func_vars[i_tensor].eval(session=sess)
net_list_lock.release() # 释放锁
# 下面三个function分别为整合 train、 update_target 、 q_values
# Create callable functions
train = U.function(
inputs=[
obs_t_input,
act_t_ph,
rew_t_ph,
obs_tp1_input,
done_mask_ph,
importance_weights_ph
],
outputs=td_error,
updates=[optimize_expr]
)
# update_target操作没有输入输出
update_target = U.function([], [], updates=[update_target_expr])
q_values = U.function([obs_t_input], q_t)
return act_f, train, update_target, init_actor_qfunc, update_actor_qfunc, {'q_values': q_values}