def q_train(make_obs_ph_n,
act_space_n,
q_index,
q_func,
optimizer,
grad_norm_clipping=None,
local_q_func=False,
scope="trainer",
reuse=None,
num_units=64):
with tf.variable_scope(scope, reuse=reuse):
# create distribtuions
act_pdtype_n = [make_pdtype(act_space) for act_space in act_space_n]
# set up placeholders
obs_ph_n = make_obs_ph_n
act_ph_n = [
act_pdtype_n[i].sample_placeholder([None], name="action" + str(i))
for i in range(len(act_space_n))
]
target_ph = tf.placeholder(tf.float32, [None], name="target")
q_input = tf.concat(obs_ph_n + act_ph_n, 1)
if local_q_func:
q_input = tf.concat([obs_ph_n[q_index], act_ph_n[q_index]], 1)
q = q_func(q_input, 1, scope="q_func", num_units=num_units)[:, 0]
q_func_vars = U.scope_vars(U.absolute_scope_name("q_func"))
q_loss = tf.reduce_mean(tf.square(q - target_ph))
# viscosity solution to Bellman differential equation in place of an initial condition
q_reg = tf.reduce_mean(tf.square(q))
loss = q_loss #+ 1e-3 * q_reg
optimize_expr = U.minimize_and_clip(optimizer, loss, q_func_vars,
grad_norm_clipping)
# Create callable functions
train = U.function(inputs=obs_ph_n + act_ph_n + [target_ph],
outputs=loss,
updates=[optimize_expr])
q_values = U.function(obs_ph_n + act_ph_n, q)
# target network
target_q = q_func(q_input,
1,
scope="target_q_func",
num_units=num_units)[:, 0]
target_q_func_vars = U.scope_vars(
U.absolute_scope_name("target_q_func"))
update_target_q = make_update_exp(q_func_vars, target_q_func_vars)
target_q_values = U.function(obs_ph_n + act_ph_n, target_q)
return train, update_target_q, {
'q_values': q_values,
'target_q_values': target_q_values
}
def p_train(make_obs_ph_n,
act_space_n,
agent_idx,
p_func,
q_func,
optimizer,
grad_norm_clipping=None,
local_q_func=False,
num_units=64,
scope="trainer",
reuse=None):
"""
:param make_obs_ph_n:
:param act_space_n:
:param agent_idx:
:param p_func: in base maddpg code = mlp_model
:param q_func: in base maddpg code = mlp_model
:param optimizer:
:param grad_norm_clipping:
:param local_q_func:
:param num_units:
:param scope:
:param reuse:
:return:
"""
with tf.variable_scope(scope, reuse=reuse):
# create distribtuions
act_pdtype_n = [make_pdtype(act_space) for act_space in act_space_n]
# set up placeholders
obs_ph_n = [tf.layers.flatten(obs_ph) for obs_ph in make_obs_ph_n]
act_ph_n = [
act_pdtype_n[i].sample_placeholder([None], name="action" + str(i))
for i in range(len(act_space_n))
]
p_input = obs_ph_n[agent_idx]
p = p_func(p_input,
int(act_pdtype_n[agent_idx].param_shape()[0]),
scope="p_func",
num_units=num_units)
p_func_vars = U.scope_vars(U.absolute_scope_name("p_func"))
# wrap parameters in distribution
act_pd = act_pdtype_n[agent_idx].pdfromflat(p)
act_sample = act_pd.sample()
p_reg = tf.reduce_mean(tf.square(act_pd.flatparam()))
act_input_n = act_ph_n + []
act_input_n[agent_idx] = act_pd.sample() #act_pd.mode() #
q_input = tf.concat(obs_ph_n + act_input_n, 1)
q = q_func(q_input,
1,
scope="q_func" + str(1),
reuse=True,
num_units=num_units)[:, 0]
loss = -tf.reduce_mean(q) + p_reg * 1e-3
optimize_expr = U.minimize_and_clip(optimizer, loss, p_func_vars,
grad_norm_clipping)
# Create callable functions
train = U.function(inputs=make_obs_ph_n + act_ph_n,
outputs=loss,
updates=[optimize_expr])
act = U.function(inputs=[make_obs_ph_n[agent_idx]], outputs=act_sample)
p_values = U.function([make_obs_ph_n[agent_idx]], p)
# target network
target_p = p_func(p_input,
int(act_pdtype_n[agent_idx].param_shape()[0]),
scope="target_p_func",
num_units=num_units)
target_p_func_vars = U.scope_vars(
U.absolute_scope_name("target_p_func"))
update_target_p = make_update_exp(p_func_vars, target_p_func_vars)
target_act_sample = act_pdtype_n[agent_idx].pdfromflat(
target_p).sample()
target_act = U.function(inputs=[make_obs_ph_n[agent_idx]],
outputs=target_act_sample)
return act, train, update_target_p, {
'p_values': p_values,
'target_act': target_act
}
def __init__(self, input_space, act_space, scope, args):
self.input_shape = input_space
self.act_space = act_space
self.scope = scope
self.replay_buffer = ReplayBuffer(1e6)
self.max_replay_buffer_len = args.batch_size * args.max_episode_len
self.replay_sample_index = None
self.optimizer = tf.train.AdamOptimizer(learning_rate=args.lr)
self.grad_norm_clipping = 0.5
with tf.variable_scope(self.scope):
act_pdtype = make_pdtype(act_space)
# act_ph = act_pdtype.sample_placeholder([None], name= "action")
act_ph = tf.placeholder(tf.float32, shape=(None, 1))
if args.game == "RoboschoolPong-v1":
obs_ph = tf.placeholder(tf.float32,
shape=(None, input_space.shape[0]))
elif args.game == "Pong-2p-v0":
obs_ph = tf.placeholder(tf.float32,
shape=(None, input_space.shape[0],
input_space.shape[1],
input_space.shape[2]))
q_target = tf.placeholder(tf.float32, shape=(None, ))
#build the world representation z
z = conv_model(obs_ph, 20, scope="world_model")
p_input = z
p = mlp_model(p_input, 2, scope="p_func")
p_func_vars = U.scope_vars(U.absolute_scope_name("p_func"))
act_pd = act_pdtype.pdfromflat(p)
act_sample = act_pd.sample()
p_reg = tf.reduce_mean(tf.square(act_pd.flatparam()))
q_input = tf.concat([z, act_sample], -1)
q = mlp_model(q_input, 1, scope="q_func")
q_func_vars = U.scope_vars(U.absolute_scope_name("q_func"))
pg_loss = -tf.reduce_mean(q)
q_loss = tf.reduce_mean(tf.square(q - q_target))
# q_reg = tf.reduce_mean(tf.square(q))
q_optimize_expr = U.minimize_and_clip(self.optimizer, q_loss,
q_func_vars,
self.grad_norm_clipping)
p_loss = pg_loss + p_reg * 1e-3
p_optimize_expr = U.minimize_and_clip(self.optimizer, p_loss,
p_func_vars,
self.grad_norm_clipping)
p_values = U.function([obs_ph], p)
target_p = mlp_model(z, 2, scope="target_p_func")
target_p_func_vars = U.scope_vars(
U.absolute_scope_name("target_p_func"))
target_q = mlp_model(q_input, 1, scope="target_q_func")
target_q_func_vars = U.scope_vars(
U.absolute_scope_name("target_q_func"))
target_act_sample = act_pdtype.pdfromflat(target_p).sample()
self.update_target_p = make_update_exp(p_func_vars,
target_p_func_vars)
self.update_target_q = make_update_exp(q_func_vars,
target_q_func_vars)
self.act = U.function(inputs=[obs_ph], outputs=act_sample)
self.target_act = U.function(inputs=[obs_ph],
outputs=target_act_sample)
self.p_train = U.function(inputs=[obs_ph] + [act_ph],
outputs=p_loss,
updates=[p_optimize_expr])
self.q_train = U.function(inputs=[obs_ph] + [act_ph] + [q_target],
outputs=q_loss,
updates=[q_optimize_expr])
self.q_values = U.function([obs_ph] + [act_ph], q)
self.target_q_values = U.function([obs_ph] + [act_ph], target_q)
def build_train(make_obs_ph,
q_func,
num_actions,
num_action_streams,
batch_size,
optimizer_name,
learning_rate,
grad_norm_clipping=None,
gamma=0.99,
double_q=True,
scope="deepq",
reuse=None,
loss_type="L2"):
"""Creates the act 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
total number of sub-actions to be represented at the output
num_action_streams: int
specifies the number of action branches in action value (or advantage) function representation
batch_size: int
size of the sampled mini-batch from the replay buffer
reuse: bool
whether or not to reuse the graph variables
optimizer: tf.train.Optimizer
optimizer to use for deep Q-learning
grad_norm_clipping: float or None
clip graident 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. BDQ uses it.
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.
Returns
-------
act: (tf.Variable, bool, float) -> tf.Variable
function to select an action given an 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.
"""
act_f, q_f = build_act(make_obs_ph,
q_func,
num_actions,
num_action_streams,
scope=scope,
reuse=reuse)
with tf.variable_scope(scope, reuse=reuse):
# Set up placeholders
obs_t_input = U.ensure_tf_input(make_obs_ph("obs_t"))
act_t_ph = tf.placeholder(tf.int32, [None, num_action_streams],
name="action")
rew_t_ph = tf.placeholder(tf.float32, [None], name="reward")
obs_tp1_input = U.ensure_tf_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")
# 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 = U.scope_vars(U.absolute_scope_name("q_func"))
# Target Q-network evalution
q_tp1 = q_func(obs_tp1_input.get(), num_actions, scope="target_q_func")
target_q_func_vars = U.scope_vars(
U.absolute_scope_name("target_q_func"))
if double_q:
selection_q_tp1 = q_func(obs_tp1_input.get(),
num_actions,
scope="q_func",
reuse=True)
else:
selection_q_tp1 = q_tp1
num_actions_pad = num_actions // num_action_streams
q_values = []
for dim in range(num_action_streams):
selected_a = tf.squeeze(
tf.slice(act_t_ph, [0, dim], [batch_size, 1])) # TODO better?
q_values.append(
tf.reduce_sum(tf.one_hot(selected_a, num_actions_pad) *
q_t[dim],
axis=1))
target_q_values = []
for dim in range(num_action_streams):
selected_a = tf.argmax(selection_q_tp1[dim], axis=1)
selected_q = tf.reduce_sum(
tf.one_hot(selected_a, num_actions_pad) * q_tp1[dim], axis=1)
masked_selected_q = (1.0 - done_mask_ph) * selected_q
target_q = rew_t_ph + gamma * masked_selected_q
target_q_values.append(target_q)
if optimizer_name == "Adam":
optimizer = tf.train.AdamOptimizer(learning_rate)
else:
assert False, 'unsupported optimizer ' + str(optimizer_name)
if loss_type == "L2":
loss_function = tf.square
elif loss_type == "Huber":
loss_function = U.huber_loss
else:
assert False, 'unsupported loss type ' + str(loss_type)
stream_losses = []
for dim in range(num_action_streams):
dim_td_error = q_values[dim] - tf.stop_gradient(
target_q_values[dim])
dim_loss = loss_function(dim_td_error)
# Scaling of learning based on importance sampling weights is optional, either way works
stream_losses.append(
tf.reduce_mean(dim_loss *
importance_weights_ph)) # with scaling
if dim == 0:
td_error = tf.abs(dim_td_error)
else:
td_error += tf.abs(dim_td_error)
mean_loss = sum(stream_losses) / num_action_streams
optimize_expr = U.minimize_and_clip(
optimizer,
mean_loss,
var_list=q_func_vars,
total_n_streams=(num_action_streams),
clip_val=grad_norm_clipping)
optimize_expr = [optimize_expr]
# Target Q-network parameters are periodically updated with the Q-network's
update_target_expr = []
for var, var_target in zip(
sorted(q_func_vars, key=lambda v: v.name),
sorted(target_q_func_vars, key=lambda v: v.name)):
update_target_expr.append(var_target.assign(var))
update_target_expr = tf.group(*update_target_expr)
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 = U.function([], [], updates=[update_target_expr])
q_values = U.function([obs_t_input], q_t)
return act_f, q_f, train, update_target, {'q_values': q_values}
