def __init__(self, name, observation_shape, hid_size, num_hid_layers):
with tf.variable_scope(name):
self.scope = tf.get_variable_scope().name
observations_ph = U.get_placeholder(name='ob',
dtype=tf.float32,
shape=[None] +
list(observation_shape))
with tf.variable_scope('obfilter'):
self.ob_rms = RunningMeanStd(shape=observation_shape)
with tf.variable_scope('vf'):
last_out = tf.clip_by_value(
(observations_ph - self.ob_rms.mean) / self.ob_rms.std,
-5.0, 5.0)
for i in range(num_hid_layers):
last_out = tf.nn.tanh(
tf.layers.dense(
last_out,
hid_size,
name='fc%i' % (i + 1),
kernel_initializer=U.normc_initializer(1.0)))
self.vpred = tf.layers.dense(
last_out,
1,
name='final',
kernel_initializer=U.normc_initializer(1.0))[:, 0]
self.predict = U.function([observations_ph], self.vpred)
def _init(self, ob_space, ac_space, kind):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape))
x = ob / 255.0
if kind == 'small': # from A3C paper
x = tf.nn.relu(U.conv2d(x, 16, "l1", [8, 8], [4, 4], pad="VALID"))
x = tf.nn.relu(U.conv2d(x, 32, "l2", [4, 4], [2, 2], pad="VALID"))
x = U.flattenallbut0(x)
x = tf.nn.relu(U.dense(x, 256, 'lin', U.normc_initializer(1.0)))
elif kind == 'large': # Nature DQN
x = tf.nn.relu(U.conv2d(x, 32, "l1", [8, 8], [4, 4], pad="VALID"))
x = tf.nn.relu(U.conv2d(x, 64, "l2", [4, 4], [2, 2], pad="VALID"))
x = tf.nn.relu(U.conv2d(x, 64, "l3", [3, 3], [1, 1], pad="VALID"))
x = U.flattenallbut0(x)
x = tf.nn.relu(U.dense(x, 512, 'lin', U.normc_initializer(1.0)))
else:
raise NotImplementedError
logits = U.dense(x, pdtype.param_shape()[0], "logits", U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(logits)
self.vpred = U.dense(x, 1, "value", U.normc_initializer(1.0))[:,0]
self.state_in = []
self.state_out = []
stochastic = tf.compat.v1.placeholder(dtype=tf.bool, shape=())
ac = self.pd.sample() # XXX
self._act = U.function([stochastic, ob], [ac, self.vpred])
def build_graph(self, obs_ph, acs_ph, reuse=False):
with tf.variable_scope(self.scope):
if reuse:
tf.get_variable_scope().reuse_variables()
with tf.variable_scope("obfilter"):
self.obs_rms = RunningMeanStd(shape=self.observation_shape)
obs = (obs_ph - self.obs_rms.mean / self.obs_rms.std)
last_out = obs
last_out = tf.nn.tanh(
U.conv2d(last_out, 64, 'vfconv1', (7, 7), (3, 3), pad='VALID'))
last_out = tf.nn.tanh(
U.conv2d(last_out, 64, 'vfconv2', (5, 5), (2, 2), pad='VALID'))
last_out = tf.nn.tanh(
U.conv2d(last_out, 64, 'vfconv3', (3, 3), (1, 1), pad='VALID'))
last_out = tf.nn.tanh(
U.conv2d(last_out, 64, 'vfconv4', (3, 3), (1, 1), pad='VALID'))
last_out = tf.reshape(last_out, tf.convert_to_tensor([-1,
784 * 4]))
last_out = tf.nn.tanh(
tf.layers.dense(last_out,
512,
kernel_initializer=U.normc_initializer(1.0)))
last_out = tf.concat([last_out, acs_ph], axis=1)
logits = tf.layers.dense(
last_out + self.num_actions,
1,
kernel_initializer=U.normc_initializer(1.0))
return logits
def __init__(self, ob_dim, ac_dim): #pylint: disable=W0613
#X = tf.placeholder(tf.float32, shape=[None, ob_dim*2+ac_dim*2+2]) # batch of observations
X = tf.placeholder(tf.float32, shape=[None, ob_dim*2+2]) # batch of observations
vtarg_n = tf.placeholder(tf.float32, shape=[None], name='vtarg')
wd_dict = {}
h1 = tf.nn.elu(dense(X, 64, "h1", weight_init=U.normc_initializer(1.0), bias_init=0, weight_loss_dict=wd_dict))
h2 = tf.nn.elu(dense(h1, 64, "h2", weight_init=U.normc_initializer(1.0), bias_init=0, weight_loss_dict=wd_dict))
vpred_n = dense(h2, 1, "hfinal", weight_init=U.normc_initializer(1.0), bias_init=0, weight_loss_dict=wd_dict)[:,0]
sample_vpred_n = vpred_n + tf.random_normal(tf.shape(vpred_n))
wd_loss = tf.get_collection("vf_losses", None)
loss = U.mean(tf.square(vpred_n - vtarg_n)) + tf.add_n(wd_loss)
loss_sampled = U.mean(tf.square(vpred_n - tf.stop_gradient(sample_vpred_n)))
self._predict = U.function([X], vpred_n)
optim = kfac.KfacOptimizer(learning_rate=0.001, cold_lr=0.001*(1-0.9), momentum=0.9, \
clip_kl=0.3, epsilon=0.1, stats_decay=0.95, \
async=1, kfac_update=2, cold_iter=50, \
weight_decay_dict=wd_dict, max_grad_norm=None)
vf_var_list = []
for var in tf.trainable_variables():
if "vf" in var.name:
vf_var_list.append(var)
update_op, self.q_runner = optim.minimize(loss, loss_sampled, var_list=vf_var_list)
self.do_update = U.function([X, vtarg_n], update_op) #pylint: disable=E1101
U.initialize() # Initialize uninitialized TF variables
def _init(self, ob_space, ac_space):
self.pdtype = distributions.make_pdtype(ac_space)
ob = U.get_placeholder(name='ob', dtype=tf.int32, shape=[None] + list(ob_space.shape))
next_blocks, my_grid, opp_grid = tf.split(ob, [16, 12 * 6, 12 * 6], axis=1)
with tf.variable_scope('next_blocks'):
next_blocks = tf.one_hot(next_blocks, depth=5)
next_blocks = U.flattenallbut0(next_blocks)
next_blocks = tf.nn.leaky_relu(tf.layers.dense(next_blocks, 12, name='l1', kernel_initializer=U.normc_initializer(1.0)), alpha=0.1)
next_blocks = tf.nn.leaky_relu(tf.layers.dense(next_blocks, 12, name='l2', kernel_initializer=U.normc_initializer(1.0)), alpha=0.1)
with tf.variable_scope('grids', reuse=False):
my_grid = _grid_cnn(my_grid)
with tf.variable_scope('grids', reuse=True):
opp_grid = _grid_cnn(opp_grid)
x = tf.concat([next_blocks, my_grid, opp_grid], axis=1)
x = tf.nn.leaky_relu(tf.layers.dense(x, 64, name='lin', kernel_initializer=U.normc_initializer(1.0)), alpha=0.1)
logits = tf.layers.dense(x, self.pdtype.param_shape()[0], name='logits', kernel_initializer=U.normc_initializer(0.01))
self.pd = self.pdtype.pdfromflat(logits)
self.vpred = tf.layers.dense(x, 1, name='value', kernel_initializer=U.normc_initializer(1.0))[:, 0]
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self._act = U.function([stochastic, ob], [ac, self.vpred])
def _init(self, ob_space, ac_space):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape))
obscaled = ob / 255.0
with tf.variable_scope("pol"):
x = obscaled
x = tf.nn.relu(U.conv2d(x, 8, "l1", [8, 8], [4, 4], pad="VALID"))
x = tf.nn.relu(U.conv2d(x, 16, "l2", [4, 4], [2, 2], pad="VALID"))
x = U.flattenallbut0(x)
x = tf.nn.relu(U.dense(x, 128, 'lin', U.normc_initializer(1.0)))
logits = U.dense(x, pdtype.param_shape()[0], "logits", U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(logits)
with tf.variable_scope("vf"):
x = obscaled
x = tf.nn.relu(U.conv2d(x, 8, "l1", [8, 8], [4, 4], pad="VALID"))
x = tf.nn.relu(U.conv2d(x, 16, "l2", [4, 4], [2, 2], pad="VALID"))
x = U.flattenallbut0(x)
x = tf.nn.relu(U.dense(x, 128, 'lin', U.normc_initializer(1.0)))
self.vpred = U.dense(x, 1, "value", U.normc_initializer(1.0))
self.vpredz = self.vpred
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = self.pd.sample() # XXX
self._act = U.function([stochastic, ob], [ac, self.vpred])
def _init(self, ob_space, ac_space, hid_size, num_hid_layers, gaussian_fixed_var=True):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape))
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape=ob_space.shape)
obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(U.dense(last_out, hid_size, "vffc%i"%(i+1), weight_init=U.normc_initializer(1.0)))
self.vpred = U.dense(last_out, 1, "vffinal", weight_init=U.normc_initializer(1.0))[:,0]
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(U.dense(last_out, hid_size, "polfc%i"%(i+1), weight_init=U.normc_initializer(1.0)))
if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
mean = U.dense(last_out, pdtype.param_shape()[0]//2, "polfinal", U.normc_initializer(0.01))
logstd = tf.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0]//2], initializer=tf.zeros_initializer())
pdparam = U.concatenate([mean, mean * 0.0 + logstd], axis=1)
else:
pdparam = U.dense(last_out, pdtype.param_shape()[0], "polfinal", U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self._act = U.function([stochastic, ob], [ac, self.vpred])
def _init(self, ob_space, ac_space, kind):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape))
x = ob / 255.0
if kind == 'small': # from A3C paper
x = tf.nn.relu(U.conv2d(x, 16, "l1", [8, 8], [4, 4], pad="VALID"))
x = tf.nn.relu(U.conv2d(x, 32, "l2", [4, 4], [2, 2], pad="VALID"))
x = U.flattenallbut0(x)
x = tf.nn.relu(tf.layers.dense(x, 256, name='lin', kernel_initializer=U.normc_initializer(1.0)))
elif kind == 'large': # Nature DQN
x = tf.nn.relu(U.conv2d(x, 32, "l1", [8, 8], [4, 4], pad="VALID"))
x = tf.nn.relu(U.conv2d(x, 64, "l2", [4, 4], [2, 2], pad="VALID"))
x = tf.nn.relu(U.conv2d(x, 64, "l3", [3, 3], [1, 1], pad="VALID"))
x = U.flattenallbut0(x)
x = tf.nn.relu(tf.layers.dense(x, 512, name='lin', kernel_initializer=U.normc_initializer(1.0)))
else:
raise NotImplementedError
logits = tf.layers.dense(x, pdtype.param_shape()[0], name='logits', kernel_initializer=U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(logits)
self.vpred = tf.layers.dense(x, 1, name='value', kernel_initializer=U.normc_initializer(1.0))[:,0]
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = self.pd.sample() # XXX
self._act = U.function([stochastic, ob], [ac, self.vpred])
def __init__(self, ob_dim, ac_dim):
# Here we'll construct a bunch of expressions, which will be used in two places:
# (1) When sampling actions
# (2) When computing loss functions, for the policy update
# Variables specific to (1) have the word "sampled" in them,
# whereas variables specific to (2) have the word "old" in them
ob_no = tf.placeholder(tf.float32, shape=[None, ob_dim*2], name="ob") # batch of observations
oldac_na = tf.placeholder(tf.float32, shape=[None, ac_dim], name="ac") # batch of actions previous actions
oldac_dist = tf.placeholder(tf.float32, shape=[None, ac_dim*2], name="oldac_dist") # batch of actions previous action distributions
adv_n = tf.placeholder(tf.float32, shape=[None], name="adv") # advantage function estimate
wd_dict = {}
h1 = tf.nn.tanh(dense(ob_no, 64, "h1", weight_init=U.normc_initializer(1.0), bias_init=0.0, weight_loss_dict=wd_dict))
h2 = tf.nn.tanh(dense(h1, 64, "h2", weight_init=U.normc_initializer(1.0), bias_init=0.0, weight_loss_dict=wd_dict))
mean_na = dense(h2, ac_dim, "mean", weight_init=U.normc_initializer(0.1), bias_init=0.0, weight_loss_dict=wd_dict) # Mean control output
self.wd_dict = wd_dict
self.logstd_1a = logstd_1a = tf.get_variable("logstd", [ac_dim], tf.float32, tf.zeros_initializer()) # Variance on outputs
logstd_1a = tf.expand_dims(logstd_1a, 0)
std_1a = tf.exp(logstd_1a)
std_na = tf.tile(std_1a, [tf.shape(mean_na)[0], 1])
ac_dist = tf.concat([tf.reshape(mean_na, [-1, ac_dim]), tf.reshape(std_na, [-1, ac_dim])], 1)
sampled_ac_na = tf.random_normal(tf.shape(ac_dist[:,ac_dim:])) * ac_dist[:,ac_dim:] + ac_dist[:,:ac_dim] # This is the sampled action we'll perform.
logprobsampled_n = - tf.reduce_sum(tf.log(ac_dist[:,ac_dim:]), axis=1) - 0.5 * tf.log(2.0*np.pi)*ac_dim - 0.5 * tf.reduce_sum(tf.square(ac_dist[:,:ac_dim] - sampled_ac_na) / (tf.square(ac_dist[:,ac_dim:])), axis=1) # Logprob of sampled action
logprob_n = - tf.reduce_sum(tf.log(ac_dist[:,ac_dim:]), axis=1) - 0.5 * tf.log(2.0*np.pi)*ac_dim - 0.5 * tf.reduce_sum(tf.square(ac_dist[:,:ac_dim] - oldac_na) / (tf.square(ac_dist[:,ac_dim:])), axis=1) # Logprob of previous actions under CURRENT policy (whereas oldlogprob_n is under OLD policy)
kl = tf.reduce_mean(kl_div(oldac_dist, ac_dist, ac_dim))
#kl = .5 * tf.reduce_mean(tf.square(logprob_n - oldlogprob_n)) # Approximation of KL divergence between old policy used to generate actions, and new policy used to compute logprob_n
surr = - tf.reduce_mean(adv_n * logprob_n) # Loss function that we'll differentiate to get the policy gradient
surr_sampled = - tf.reduce_mean(logprob_n) # Sampled loss of the policy
self._act = U.function([ob_no], [sampled_ac_na, ac_dist, logprobsampled_n]) # Generate a new action and its logprob
#self.compute_kl = U.function([ob_no, oldac_na, oldlogprob_n], kl) # Compute (approximate) KL divergence between old policy and new policy
self.compute_kl = U.function([ob_no, oldac_dist], kl)
self.update_info = ((ob_no, oldac_na, adv_n), surr, surr_sampled) # Input and output variables needed for computing loss
U.initialize() # Initialize uninitialized TF variables
def _create_logit_value(self,
action_layer,
value_layer,
gaussian_fixed_var=False):
# actor
if gaussian_fixed_var and isinstance(self.ac_space, gym.spaces.Box):
mean = U.dense(action_layer,
self.pdtype.param_shape()[0] // 2, "polfinal",
U.normc_initializer(0.01))
logstd = tf.get_variable(
name="logstd",
shape=[1, self.pdtype.param_shape()[0] // 2],
initializer=tf.zeros_initializer())
pdparam = U.concatenate([mean, mean * 0.0 + logstd], axis=1)
else:
pdparam = U.dense(action_layer,
self.pdtype.param_shape()[0], "polfinal",
U.normc_initializer(0.01))
self.pd = self.pdtype.pdfromflat(pdparam)
self.ac = U.switch(self.stochastic, self.pd.sample(), self.pd.mode())
# critic
self.vpred = U.dense(value_layer,
1,
"vffinal",
weight_init=U.normc_initializer(1.0))[:, 0]
def _create_network(self):
x = self.ob
# create ob filter
if self.ob_filter:
self.ob_rms = RunningMeanStd(shape=self.ob_space.shape)
x = tf.clip_by_value(
(self.ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
# actor
l = x
l = tf.nn.tanh(
U.dense(l, 32, "a_1", weight_init=U.normc_initializer(1.0)))
l = tf.nn.tanh(
U.dense(l, 32, "a_2", weight_init=U.normc_initializer(1.0)))
action_layer = l
# critic
l = x
l = tf.nn.tanh(
U.dense(l, 32, "c_1", weight_init=U.normc_initializer(1.0)))
l = tf.nn.tanh(
U.dense(l, 32, "c_2", weight_init=U.normc_initializer(1.0)))
value_layer = l
self._create_logit_value(action_layer, value_layer,
self.gaussian_fixed_var)
def _init(self,
in_dim,
out_dim,
hid_size,
num_hid_layers,
last_init_size=0.01,
name='ff'):
# state_dim: dimension of input/output state from previous/root encoder
self.params = []
self.num_hid_layers = num_hid_layers
self.intin_dim = in_dim
last_out_dim = in_dim
for i in range(num_hid_layers):
w, b = dense_params(last_out_dim,
hid_size,
name + "%i" % (i + 1),
weight_init=U.normc_initializer(1.0))
self.params.append([w, b])
last_out_dim = hid_size
w, b = dense_params(last_out_dim,
out_dim,
name + "_out",
weight_init=U.normc_initializer(last_init_size))
self.params.append([w, b])
def __init__(self, sess, ob_space, ac_space, nbatch, nsteps, reuse=False): #pylint: disable=W0613
nh, nw, nc = ob_space.shape
ob_shape = (nbatch, nh, nw, nc)
nact = ac_space.n
X = tf.placeholder(tf.float32, ob_shape) #obs
print(ob_shape)
self.pdtype = pdtype = make_pdtype(ac_space)
with tf.variable_scope("model", reuse=reuse):
'''
h = conv(X, 'c1', nf=64, rf=3, stride=1, init_scale=np.sqrt(2), pad="SAME")
h2 = conv(h, 'c2', nf=64, rf=3, stride=1, init_scale=np.sqrt(2), pad="SAME")
h3 = conv(h2, 'c3', nf=128, rf=3, stride=1, init_scale=np.sqrt(2), pad="SAME")
h3 = conv_to_fc(h3)
h4 = fc(h3, 'fc1', nh=512, init_scale=np.sqrt(2))
hh = conv(X, 'xc1', nf=64, rf=3, stride=1, init_scale=np.sqrt(2), pad="SAME")
hh2 = conv(hh, 'xc2', nf=64, rf=3, stride=1, init_scale=np.sqrt(2), pad="SAME")
hh3 = conv(hh2, 'xc3', nf=128, rf=3, stride=1, init_scale=np.sqrt(2), pad="SAME")
hh3 = conv_to_fc(hh3)
hh4 = fc(hh3, 'xfc1', nh=512, init_scale=np.sqrt(2))
pi = fc(h4, 'pi', nact, act=lambda x:x, init_scale=0.01)
vf = fc(hh4, 'v', 1, act=lambda x:x)[:,0]
'''
x = tf.nn.relu(U.conv2d(X, 32, "l1", [3, 3], [1, 1], pad="SAME"))
x = tf.nn.relu(U.conv2d(x, 64, "l2", [3, 3], [1, 1], pad="SAME"))
x = tf.nn.relu(U.conv2d(x, 128, "l3", [3, 3], [1, 1], pad="SAME"))
x = U.flattenallbut0(x)
x = tf.nn.relu(U.dense(x, 512, 'lin', U.normc_initializer(1.0)))
y = tf.nn.relu(U.conv2d(X, 32, "yl1", [3, 3], [1, 1], pad="SAME"))
y = tf.nn.relu(U.conv2d(y, 64, "yl2", [3, 3], [1, 1], pad="SAME"))
y = tf.nn.relu(U.conv2d(y, 128, "yl3", [3, 3], [1, 1], pad="SAME"))
y = U.flattenallbut0(y)
y = tf.nn.relu(U.dense(y, 512, 'ylin', U.normc_initializer(1.0)))
pi = U.dense(x,
pdtype.param_shape()[0], "logits",
U.normc_initializer(0.01))
vf = U.dense(y, 1, "value", U.normc_initializer(1.0))[:, 0]
self.pd = self.pdtype.pdfromflat(pi)
a0 = self.pd.sample()
neglogp0 = self.pd.neglogp(a0)
self.initial_state = None
def step(ob, *_args, **_kwargs):
a, v, neglogp = sess.run([a0, vf, neglogp0], {X: ob})
return a, v, self.initial_state, neglogp
def value(ob, *_args, **_kwargs):
return sess.run(vf, {X: ob})
self.X = X
self.pi = pi
self.vf = vf
self.step = step
self.value = value
def _init(self,
in_dim,
out_dim,
hid_size,
num_hid_layers,
last_init_size=0.01):
# state_dim: dimension of input/output state from previous/root encoder
self.params = []
self.num_hid_layers = num_hid_layers
self.intin_dim = in_dim - 1
last_out_dim = in_dim - 1
for i in range(num_hid_layers):
w, b = dense_params(last_out_dim,
hid_size,
"ff%i" % (i + 1),
weight_init=U.normc_initializer(1.0))
logmask = tf.get_variable(
name="logmask%i" % (i + 1),
shape=[1],
initializer=tf.constant_initializer(-1.0))
self.params.append([w, b, logmask])
last_out_dim = hid_size
w, b = dense_params(last_out_dim,
out_dim,
"ff_out",
weight_init=U.normc_initializer(last_init_size))
logmask = tf.get_variable(name="logmask_out",
shape=[1],
initializer=tf.constant_initializer(-1.0))
self.params.append([w, b, logmask])
def _init(self,
ob_space,
ac_space,
hid_size,
num_hid_layers,
exploration_rate,
gaussian_fixed_var=True):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob",
dtype=tf.float32,
shape=[sequence_length] + list(ob_space.shape))
# with tf.variable_scope("obfilter"):
# self.ob_rms = RunningMeanStd(shape=ob_space.shape)
# obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
obz = ob
valueFunction = Sequential()
valueFunction.add(InputLayer(input_tensor=obz))
valueFunction.add(Dense(64, activation='tanh'))
valueFunction.add(Dense(64, activation='tanh'))
self.vpred = self.dense(x=valueFunction.output,
size=1,
name="vffinal",
weight_init=U.normc_initializer(1.0),
bias=True)[:, 0]
model = Sequential()
model.add(InputLayer(input_tensor=obz))
model.add(Dense(64, activation='tanh'))
model.add(Dense(64, activation='tanh'))
model.add(Dense(23))
model.load_weights("neural_kick")
if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
mean = model.output
logstd = tf.get_variable(
name="logstd",
shape=[1, pdtype.param_shape()[0] // 2],
initializer=tf.constant_initializer(exploration_rate))
pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
else:
pdparam = tf.layers.dense(model.output,
pdtype.param_shape()[0], "polfinal",
U.normc_initializer(0.01))
my_var = tf.strided_slice(mean, [0], [1], [1], shrink_axis_mask=1)
my_var_out = tf.identity(my_var, name='output_node')
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self._act = U.function([stochastic, ob], [ac, self.vpred])
def __init__(self, ob_dim, ac_dim):
# Here we'll construct a bunch of expressions, which will be used in two places:
# (1) When sampling actions
# (2) When computing loss functions, for the policy update
# Variables specific to (1) have the word "sampled" in them,
# whereas variables specific to (2) have the word "old" in them
ob_no = tf.placeholder(tf.float32, shape=[None, ob_dim*2], name="ob") # batch of observations
oldac_na = tf.placeholder(tf.float32, shape=[None, ac_dim], name="ac") # batch of actions previous actions
oldac_dist = tf.placeholder(tf.float32, shape=[None, ac_dim*2], name="oldac_dist") # batch of actions previous action distributions
adv_n = tf.placeholder(tf.float32, shape=[None], name="adv") # advantage function estimate
oldlogprob_n = tf.placeholder(tf.float32, shape=[None], name='oldlogprob') # log probability of previous actions
wd_dict = {}
h1 = tf.nn.tanh(dense(ob_no, 64, "h1", weight_init=U.normc_initializer(1.0), bias_init=0.0, weight_loss_dict=wd_dict))
h2 = tf.nn.tanh(dense(h1, 64, "h2", weight_init=U.normc_initializer(1.0), bias_init=0.0, weight_loss_dict=wd_dict))
mean_na = dense(h2, ac_dim, "mean", weight_init=U.normc_initializer(0.1), bias_init=0.0, weight_loss_dict=wd_dict) # Mean control output
self.wd_dict = wd_dict
self.logstd_1a = logstd_1a = tf.get_variable("logstd", [ac_dim], tf.float32, tf.zeros_initializer()) # Variance on outputs
logstd_1a = tf.expand_dims(logstd_1a, 0)
std_1a = tf.exp(logstd_1a)
std_na = tf.tile(std_1a, [tf.shape(mean_na)[0], 1])
ac_dist = tf.concat([tf.reshape(mean_na, [-1, ac_dim]), tf.reshape(std_na, [-1, ac_dim])], 1)
sampled_ac_na = tf.random_normal(tf.shape(ac_dist[:,ac_dim:])) * ac_dist[:,ac_dim:] + ac_dist[:,:ac_dim] # This is the sampled action we'll perform.
logprobsampled_n = - U.sum(tf.log(ac_dist[:,ac_dim:]), axis=1) - 0.5 * tf.log(2.0*np.pi)*ac_dim - 0.5 * U.sum(tf.square(ac_dist[:,:ac_dim] - sampled_ac_na) / (tf.square(ac_dist[:,ac_dim:])), axis=1) # Logprob of sampled action
logprob_n = - U.sum(tf.log(ac_dist[:,ac_dim:]), axis=1) - 0.5 * tf.log(2.0*np.pi)*ac_dim - 0.5 * U.sum(tf.square(ac_dist[:,:ac_dim] - oldac_na) / (tf.square(ac_dist[:,ac_dim:])), axis=1) # Logprob of previous actions under CURRENT policy (whereas oldlogprob_n is under OLD policy)
kl = U.mean(kl_div(oldac_dist, ac_dist, ac_dim))
#kl = .5 * U.mean(tf.square(logprob_n - oldlogprob_n)) # Approximation of KL divergence between old policy used to generate actions, and new policy used to compute logprob_n
surr = - U.mean(adv_n * logprob_n) # Loss function that we'll differentiate to get the policy gradient
surr_sampled = - U.mean(logprob_n) # Sampled loss of the policy
self._act = U.function([ob_no], [sampled_ac_na, ac_dist, logprobsampled_n]) # Generate a new action and its logprob
#self.compute_kl = U.function([ob_no, oldac_na, oldlogprob_n], kl) # Compute (approximate) KL divergence between old policy and new policy
self.compute_kl = U.function([ob_no, oldac_dist], kl)
self.update_info = ((ob_no, oldac_na, adv_n), surr, surr_sampled) # Input and output variables needed for computing loss
U.initialize() # Initialize uninitialized TF variables
def img_encoder(self, x, kind):
if kind == 'small': # from A3C paper
x = max_pool(
tf.nn.relu(U.conv2d(x, 16, "l1", [8, 8], [1, 1], pad="VALID")),
4)
x = max_pool(
tf.nn.relu(U.conv2d(x, 32, "l2", [4, 4], [1, 1], pad="VALID")),
2)
x = U.flattenallbut0(x)
x = tf.nn.relu(
tf.layers.dense(x,
256,
name='lin',
kernel_initializer=U.normc_initializer(1.0)))
elif kind == 'large': # Nature DQN
x = max_pool(
tf.nn.relu(U.conv2d(x, 32, "l1", [8, 8], [1, 1], pad="VALID")),
4)
x = max_pool(
tf.nn.relu(U.conv2d(x, 64, "l2", [4, 4], [1, 1], pad="VALID")),
2)
x = tf.nn.relu(U.conv2d(x, 64, "l3", [3, 3], [1, 1], pad="VALID"))
x = U.flattenallbut0(x)
x = tf.nn.relu(
tf.layers.dense(x,
512,
name='lin',
kernel_initializer=U.normc_initializer(1.0)))
else:
raise NotImplementedError
return x
def _init(self, ob_space, ac_space, hid_size, num_hid_layers, use_actions):
assert isinstance(ob_space, gym.spaces.Box)
self.use_actions = use_actions
sequence_length = None
if use_actions:
inp_shape = (ob_space.shape[0] + ac_space.shape[0], )
else:
inp_shape = ob_space.shape
rew_input = U.get_placeholder(name="rew_input",
dtype=tf.float32,
shape=[sequence_length] +
list(inp_shape))
with tf.variable_scope("inputfilter"):
self.inp_rms = RunningMeanStd(shape=inp_shape)
with tf.variable_scope('rew'):
input_clipped = tf.clip_by_value(
(rew_input - self.inp_rms.mean) / self.inp_rms.std, -5.0, 5.0)
last_out = input_clipped
for i in range(num_hid_layers):
last_out = tf.nn.tanh(
tf.layers.dense(
last_out,
hid_size,
name="fc%i" % (i + 1),
kernel_initializer=U.normc_initializer(1.0)))
self.reward = tf.layers.dense(
last_out,
1,
name='final',
kernel_initializer=U.normc_initializer(1.0))[:, 0]
self._rew = U.function([rew_input], [self.reward])
def _init(self,
ob_space,
ac_space,
hid_size,
num_hid_layers,
gaussian_fixed_var=True):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob",
dtype=tf.float32,
shape=[sequence_length] + list(ob_space.shape))
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape=ob_space.shape)
obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0,
5.0)
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(
U.dense(last_out,
hid_size,
"vffc%i" % (i + 1),
weight_init=U.normc_initializer(1.0)))
self.vpred = U.dense(last_out,
1,
"vffinal",
weight_init=U.normc_initializer(1.0))[:, 0]
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(
U.dense(last_out,
hid_size,
"polfc%i" % (i + 1),
weight_init=U.normc_initializer(1.0)))
if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
mean = U.dense(last_out,
pdtype.param_shape()[0] // 2, "polfinal",
U.normc_initializer(0.01))
logstd = tf.get_variable(name="logstd",
shape=[1, pdtype.param_shape()[0] // 2],
initializer=tf.zeros_initializer())
pdparam = U.concatenate([mean, mean * 0.0 + logstd], axis=1)
else:
pdparam = U.dense(last_out,
pdtype.param_shape()[0], "polfinal",
U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self._act = U.function([stochastic, ob], [ac, self.vpred])
def build_graph(self, obs_ph, acs_ph, reuse=False):
"""
obs_ph: tf tensor shape of [None,84,84,4]
acs_ph: tf tensor shape of [None,ac_dim] #one hot encoding
"""
with tf.variable_scope(self.scope):
if reuse:
tf.get_variable_scope().reuse_variables()
one_hot_ac = tf.one_hot(acs_ph, self.num_actions, dtype=tf.float32)
x = tf.concat([
obs_ph / 255.0,
tf.tile(one_hot_ac[:, None, None, :], [1, 84, 84, 1])
],
axis=3) #[None,84,84,4+ac_dim]
x = tf.nn.relu(U.conv2d(x, 32, "l1", [8, 8], [4, 4], pad="VALID"))
x = tf.nn.relu(U.conv2d(x, 64, "l2", [4, 4], [2, 2], pad="VALID"))
x = tf.nn.relu(U.conv2d(x, 64, "l3", [3, 3], [1, 1], pad="VALID"))
x = U.flattenallbut0(x)
x = tf.nn.tanh(
tf.layers.dense(x,
512,
name='lin1',
kernel_initializer=U.normc_initializer(1.0)))
logits = tf.layers.dense(
x, 1, name='lin2', kernel_initializer=U.normc_initializer(1.0))
return logits
def _init(self, ob_space, ac_space, hid_size, num_hid_layers, rnn_hid_units, gaussian_fixed_var=True):
#assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape))
# Apply rnn_to reduce history
with tf.variable_scope("vf"):
last_out = self.rnn(ob, ob_space.shape[0], rnn_hid_units)
for i in range(num_hid_layers):
last_out = U.dense(last_out, hid_size, "vf_dense%i"%i, weight_init=U.normc_initializer(1.0))
self.vpred = U.dense(last_out, 1, "vffinal", weight_init=U.normc_initializer(1.0))[:,0]
# Apply rnn_to reduce history
with tf.variable_scope("pf"):
last_out = self.rnn(ob, ob_space.shape[0], rnn_hid_units)
for i in range(num_hid_layers):
last_out = U.dense(last_out, hid_size, "pf_dense%i"%i, weight_init=U.normc_initializer(1.0))
assert gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box)
mean = U.dense(last_out, pdtype.param_shape()[0]//2, "polfinal", U.normc_initializer(0.01))
logstd = tf.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0]//2], initializer=tf.zeros_initializer())
pdparam = U.concatenate([mean, mean * 0.0 + logstd], axis=1)
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self._act = U.function([stochastic, ob], [ac, self.vpred])
def _init(self,
ob_space,
ac_space,
hid_size,
num_hid_layers,
tau,
gaussian_fixed_var=True):
assert isinstance(ob_space, gym.spaces.Box)
print('use zpmpl_Adv')
self.ac_space = ac_space
self.hid_size = hid_size
self.num_hid_layers = num_hid_layers
self.gaussian_fixed_var = gaussian_fixed_var
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
self.ob = U.get_placeholder(name="ob_adv",
dtype=tf.float32,
shape=[sequence_length] +
list(ob_space.shape))
self.ob_ = U.get_placeholder(name="adv_ob_",
dtype=tf.float32,
shape=[sequence_length] +
list(ob_space.shape))
with tf.variable_scope("obfilter_adv"):
self.ob_rms = RunningMeanStd(shape=ob_space.shape)
with tf.variable_scope('adv_vf'):
self.obz = tf.clip_by_value(
(self.ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
last_out = self.obz
for i in range(self.num_hid_layers):
last_out = tf.nn.tanh(
tf.layers.dense(
last_out,
self.hid_size,
name="adv_vffc%i" % (i + 1),
kernel_initializer=U.normc_initializer(1.0)))
self.vpred = tf.layers.dense(
last_out,
1,
name="adv_vffinal",
kernel_initializer=U.normc_initializer(1.0))[:, 0]
self.pdparam = self.build_action(self.ob)
self.pdparam_ = self.build_action(self.ob_, reuse=True)
self.pd = pdtype.pdfromflat(self.pdparam)
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self.ac = self.pd.sample()
self.ac_, _ = self.sample_()
self._act = U.function([stochastic, self.ob], [ac, self.vpred])
def __init__(self, ob_dim, ac_dim, ac_space, bins):
# Here we'll construct a bunch of expressions, which will be used in two places:
# (1) When sampling actions
# (2) When computing loss functions, for the policy update
# Variables specific to (1) have the word "sampled" in them,
# whereas variables specific to (2) have the word "old" in them
ob_no = tf.placeholder(tf.float32, shape=[None, ob_dim * 2],
name="ob") # batch of observations
oldac_na = tf.placeholder(
tf.int32, shape=[None, ac_dim],
name="ac") # batch of actions previous actions
oldac_logits = tf.placeholder(
tf.float32, shape=[None, ac_dim * bins], name="oldac_logit"
) # batch of actions previous action distributions
adv_n = tf.placeholder(tf.float32, shape=[None],
name="adv") # advantage function estimate
self.pdtype = make_pdtype(ac_space)
wd_dict = {}
# forward pass
h1 = tf.nn.tanh(
dense(ob_no,
64,
"h1",
weight_init=U.normc_initializer(1.0),
bias_init=0.0,
weight_loss_dict=wd_dict))
h2 = tf.nn.tanh(
dense(h1,
64,
"h2",
weight_init=U.normc_initializer(1.0),
bias_init=0.0,
weight_loss_dict=wd_dict))
logits_na = dense(h2,
self.pdtype.param_shape()[0],
"logits",
weight_init=U.normc_initializer(0.1),
bias_init=0.0,
weight_loss_dict=wd_dict) # Mean control
self.wd_dict = wd_dict
self.pd = self.pdtype.pdfromflat(
logits_na) # multi-categorical distributions
# sample action for control
sampled_ac_na = self.pd.sample()
# log prob for sampled actions
logprobsampled_n = -self.pd.neglogp(sampled_ac_na)
logprob_n = -self.pd.neglogp(oldac_na)
# kl div
old_pd = self.pdtype.pdfromflat(oldac_logits)
kl = U.mean(old_pd.kl(self.pd))
# surr loss
surr = -U.mean(adv_n * logprob_n)
surr_sampled = -U.mean(logprob_n)
# expressions
self._act = U.function([ob_no],
[sampled_ac_na, logits_na, logprobsampled_n])
self.compute_kl = U.function([ob_no, oldac_logits], kl)
self.update_info = ((ob_no, oldac_na, adv_n), surr, surr_sampled)
U.initialize()
def resnet(inputs, hid_size, name):
x = U.dense(inputs, hid_size, "%s_dense1"%name, weight_init=U.normc_initializer(1.0))
#x = tf.contrib.layers.batch_norm(x)
x = tf.nn.relu(x)
x = U.dense(x, hid_size, "%s_dense2"%name, weight_init=U.normc_initializer(1.0))
#x = tf.contrib.layers.batch_norm(x)
x = tf.nn.relu(x+inputs)
return x
def __init__(self, sess, ob_dim, ac_dim, vf_lr=0.001, cv_lr=0.001, reuse=False):
# Here we'll construct a bunch of expressions, which will be used in two places:
# (1) When sampling actions
# (2) When computing loss functions, for the policy update
# Variables specific to (1) have the word "sampled" in them,
# whereas variables specific to (2) have the word "old" in them
self.relaxed = False
self.X = tf.placeholder(tf.float32, shape=[None, ob_dim*2+ac_dim*2+2]) # batch of observations
self.ob_no = tf.placeholder(tf.float32, shape=[None, ob_dim*2], name="ob") # batch of observations
self.oldac_na = tf.placeholder(tf.float32, shape=[None, ac_dim], name="ac") # batch of actions previous actions
oldac_dist = tf.placeholder(tf.float32, shape=[None, ac_dim*2], name="oldac_dist") # batch of actions previous action distributions
with tf.variable_scope("model", reuse=reuse):
h1 = tf.nn.tanh(dense(self.ob_no, 64, "pi_h1", weight_init=U.normc_initializer(1.0), bias_init=0.0))
h2 = tf.nn.tanh(dense(h1, 64, "pi_h2", weight_init=U.normc_initializer(1.0), bias_init=0.0))
mean_na = dense(h2, ac_dim, "pi", weight_init=U.normc_initializer(0.1), bias_init=0.0) # Mean control output
self.logstd_1a = logstd_1a = tf.get_variable("logstd", [ac_dim], tf.float32, tf.zeros_initializer()) # Variance on outputs
logstd_1a = tf.expand_dims(logstd_1a, 0)
self.std_1a = tf.exp(logstd_1a)
self.std_na = tf.tile(self.std_1a, [tf.shape(mean_na)[0], 1])
ac_dist = tf.concat([tf.reshape(mean_na, [-1, ac_dim]), tf.reshape(self.std_na, [-1, ac_dim])], 1)
sampled_ac_na = tf.random_normal(tf.shape(ac_dist[:,ac_dim:])) * ac_dist[:,ac_dim:] + ac_dist[:,:ac_dim] # This is the sampled action we'll perform.
logprobsampled_n = - U.sum(tf.log(ac_dist[:,ac_dim:]), axis=1) - 0.5 * tf.log(2.0*np.pi)*ac_dim - 0.5 * U.sum(tf.square(ac_dist[:,:ac_dim] - sampled_ac_na) / (tf.square(ac_dist[:,ac_dim:])), axis=1) # Logprob of sampled action
self.logprob_n = - U.sum(tf.log(ac_dist[:,ac_dim:]), axis=1) - 0.5 * tf.log(2.0*np.pi)*ac_dim - 0.5 * U.sum(tf.square(ac_dist[:,:ac_dim] - self.oldac_na) / (tf.square(ac_dist[:,ac_dim:])), axis=1) # Logprob of previous actions under CURRENT policy (whereas oldlogprob_n is under OLD policy)
kl = U.mean(kl_div(oldac_dist, ac_dist, ac_dim))
vh1 = tf.nn.elu(dense(self.X, 64, "vf_h1", weight_init=U.normc_initializer(1.0), bias_init=0))
vh2 = tf.nn.elu(dense(vh1, 64, "vf_h2", weight_init=U.normc_initializer(1.0), bias_init=0))
vpred_n = dense(vh2, 1, "vf", weight_init=None, bias_init=0)
v0 = vpred_n[:, 0]
self.vf_optim = tf.train.AdamOptimizer(vf_lr)
def act(ob):
ac, dist, logp = sess.run([sampled_ac_na, ac_dist, logprobsampled_n], {self.ob_no: ob[None]}) # Generate a new action and its logprob
return ac[0], dist[0], logp[0]
def value(obs, x):
return sess.run(v0, {self.X: x, self.ob_no:obs})
def preproc(path):
l = pathlength(path)
al = np.arange(l).reshape(-1,1)/10.0
act = path["action_dist"].astype('float32')
X = np.concatenate([path['observation'], act, al, np.ones((l, 1))], axis=1)
return X
def predict(obs, path):
return value(obs, preproc(path))
def compute_kl(ob, dist):
return sess.run(kl, {self.ob_no: ob, oldac_dist: dist})
self.mean = mean_na
self.vf = v0
self.act = act
self.value = value
self.preproc = preproc
self.predict = predict
self.compute_kl = compute_kl
self.a0 = sampled_ac_na
def build_forward(self, state, reuse):
# build noise samples
batch_size = [state.get_shape().as_list()[0], self.input_dim]
noise_dist = tfd.Normal(loc=0., scale=1.)
noise_samples = noise_dist.sample(
batch_size) # size of [batchsize, action dim]
# build forward
last_out = state
self.meandict = meandict = []
self.logstddict = logstddict = []
with tf.variable_scope('forward', reuse=reuse):
for i in range(self.num_hid_layers):
last_out = tf.nn.tanh(
U.dense(last_out,
self.hid_size,
"polfc%i" % (i + 1),
weight_init=U.normc_initializer(1.0)))
for k in range(self.K):
mean = U.dense(last_out, self.input_dim,
"polfinal_{}".format(k),
U.normc_initializer(0.01))
logstd = tf.get_variable(name="logstd_{}".format(k),
shape=[1, self.input_dim],
initializer=tf.zeros_initializer())
meandict.append(mean)
logstddict.append(logstd)
meandicttf = tf.concat(meandict,
axis=1) # size of [batchsize, action dim * K]
logstddicttf = tf.concat(logstddict, axis=1)
# generate masks
logits = [0.0] * self.K
num_samples = self.state.shape.as_list()[0]
categorical_mask = tf.multinomial([logits], num_samples)
#print('categoricalmask', categorical_mask)
onehot_mask = tf.squeeze(tf.one_hot(categorical_mask, self.K), 0)
#print('onehotmask', onehot_mask)
onehot_mask_tiled = tf.squeeze(tf.reshape(
tf.tile(tf.expand_dims(onehot_mask, axis=2),
[1, 1, self.input_dim]), [-1, self.input_dim * self.K, 1]),
axis=2)
# select
mean_tiled = tf.multiply(
onehot_mask_tiled,
meandicttf) # size of [batchsize, action dim * K]
logstd_tiled = tf.multiply(onehot_mask_tiled, logstddicttf)
# sample action mean and logstd
mean = tf.reshape(
mean_tiled,
[-1, self.K, self.input_dim]) # size of [batchsize, K, action dim]
logstd = tf.reshape(logstd_tiled, [-1, self.K, self.input_dim])
mean_final = tf.reduce_sum(
mean, axis=1, keepdims=True) # size of [batchsize, action dim]
logstd_final = tf.reduce_sum(logstd, axis=1, keepdims=True)
# sample action
action = tf.exp(logstd_final) * noise_samples + mean_final
self.y_sample = action
def build_network(self, sess, scope, ob):
with tf.variable_scope(scope + "/obfilter"):
ob_rms = RunningMeanStd(shape=self.ob_space.shape)
with tf.variable_scope(scope + '/vf'):
obz = tf.clip_by_value((ob - ob_rms.mean) / ob_rms.std, -5.0, 5.0)
last_out = obz
for i in range(self.num_hid_layers):
last_out = tf.nn.tanh(
tf.layers.dense(
last_out,
self.hid_size,
name="fc%i" % (i + 1),
kernel_initializer=U.normc_initializer(1.0)))
vpred = tf.layers.dense(
last_out,
1,
name='final',
kernel_initializer=U.normc_initializer(1.0))[:, 0]
with tf.variable_scope(scope + '/pol'):
last_out = obz
############## tf layers version #############
for i in range(self.num_hid_layers):
last_out = tf.nn.tanh(
tf.layers.dense(
last_out,
self.hid_size,
name='fc%i' % (i + 1),
kernel_initializer=U.normc_initializer(1.0)))
mean = tf.layers.dense(
last_out,
self.ac_dim,
name='final',
kernel_initializer=U.normc_initializer(0.01))
# ############## tf learn version #############
# for i in range(self.num_hid_layers):
# last_out = tflearn.fully_connected(last_out, self.hid_size, name='fc%i'%(i+1), activation='tanh')
# mean = tflearn.fully_connected(last_out, self.ac_dim, name='final', activation='tanh')
logstd = tf.get_variable(
name="logstd",
shape=[1, self.pdtype.param_shape()[0] // 2],
initializer=tf.zeros_initializer())
pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
pd = self.pdtype.pdfromflat(pdparam)
sample_ac = pd.sample()
ac_mean = pd.mode()
return ob_rms, vpred, pd, sample_ac, ac_mean
def _init(self, ob_space, ac_space, hid_size, num_hid_layers, num_units=3, num_layers=4):
assert isinstance(ob_space, gym.spaces.Box)
nbatch_train = 1024
nbatch_vf_train = 64
nbatch_fvp_train = 205 # sub-sampled size
self.ob_train = ob_train = U.get_placeholder(name="ob_train", dtype=tf.float32, shape=[nbatch_train] + list(ob_space.shape))
self.action_train = action_train = U.get_placeholder(name='ac_train', dtype=tf.float32, shape=[nbatch_train] + list(ac_space.shape))
ob_act = U.get_placeholder(name="ob_act", dtype=tf.float32, shape=[1] + list(ob_space.shape))
action_act = U.get_placeholder(name='ac_act', dtype=tf.float32, shape=[1] + list(ac_space.shape))
self.ob_vf_train = ob_vf_train = U.get_placeholder(name="ob_vf_train", dtype=tf.float32, shape=[nbatch_vf_train] + list(ob_space.shape))
self.ob_fvp_train = ob_fvp_train = U.get_placeholder(name="ob_fvp_train", dtype=tf.float32, shape=[nbatch_fvp_train] + list(ob_space.shape))
self.ac_fvp_train = action_fvp_train = U.get_placeholder(name="ac_fvp_act", dtype=tf.float32, shape=[nbatch_fvp_train] + list(ac_space.shape))
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape=ob_space.shape)
obz_train = tf.clip_by_value((ob_train - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
obz_act = tf.clip_by_value((ob_act - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
obz_vf_train = tf.clip_by_value((ob_vf_train - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
obz_fvp_train = tf.clip_by_value((ob_fvp_train - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
# value function
last_out = obz_vf_train
with tf.variable_scope('value', reuse=False):
for i in range(num_hid_layers):
last_out = tf.nn.tanh(U.dense(last_out, hid_size, "vffc%i"%(i+1), weight_init=U.normc_initializer(1.0)))
self.vpred_train = U.dense(last_out, 1, "vffinal", weight_init=U.normc_initializer(1.0))[:,0]
last_out = obz_act
with tf.variable_scope('value', reuse=True):
for i in range(num_hid_layers):
last_out = tf.nn.tanh(U.dense(last_out, hid_size, "vffc%i"%(i+1), weight_init=U.normc_initializer(1.0)))
self.vpred_act = U.dense(last_out, 1, "vffinal", weight_init=U.normc_initializer(1.0))[:,0]
# policy
policy_train = NormalizingFlowStateModel(obz_train, action_train, name='policy', reuse=False, num_units=num_units, num_layers=num_layers)
policy_act = NormalizingFlowStateModel(obz_act, action_act, name='policy', reuse=True, num_units=num_units, num_layers=num_layers)
policy_fvp_train = NormalizingFlowStateModel(obz_fvp_train, action_fvp_train, name='policy', reuse=True, num_units=num_units, num_layers=num_layers)
self.pi_act = policy_act.y_sample #act for forward sampling
self.pi_train = policy_fvp_train.y_sample #for fvp
self.entropy_train = policy_train.entropy
self.log_prob_act = policy_act.log_prob
self.action_act = action_act
self.log_prob_train = policy_train.log_prob #logprob
self.log_prob_fvp_train = policy_fvp_train.log_prob
self.state_in = []
self.state_out = []
#stochastic = tf.placeholder(dtype=tf.bool, shape=())
#ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
#self._act = U.function([stochastic, ob], [ac, self.vpred])
self._act = U.function([ob_act], [self.pi_act, self.vpred_act])
self.ob_act = ob_act
def lstm_graph(ob_combined, input_state_combined, env):
# parse action distribution mean, logstd
# get action space type
pdtype = make_pdtype(env.action_space)
# new cell
cell = tf.contrib.rnn.LSTMCell(num_units=NUM_UNITS, name="lol")
# Initailize state with zero of batch size 1 and type float32
#c_state, m_state = tf.split(input_state_combined, [1, 1], 0 )
c_state, m_state = input_state_combined[0, :, :], input_state_combined[
1, :, :]
state = tf.tuple([c_state, m_state])
s_mean_list, s_std_list, s_logstd_list = [], [], []
for i in range(STEPS_UNROLLED):
if i > 0: tf.get_variable_scope().reuse_variables()
# normalize observation vector with rms
rms = RunningMeanStd(shape=env.observation_space.shape)
# only first step; the rest will need all (all batch, all observation space dim )
obz = tf.clip_by_value((ob_combined[i, :, :] - rms.mean) / rms.std,
-5.0, 5.0)
output, state = cell(obz, state)
output = tf.nn.tanh(
tf.layers.dense(output,
64,
name='last',
kernel_initializer=U.normc_initializer(1.0)))
# feed the output of lstm to a final FC layer
# this 'flat' vector will be split into the mean and std of a pd
pdparam = tf.layers.dense(output,
pdtype.param_shape()[0],
name='final',
kernel_initializer=U.normc_initializer(0.01))
pd = pdtype.pdfromflat(pdparam)
s_mean_list.append(pd.mean)
s_std_list.append(pd.std)
s_logstd_list.append(pd.logstd)
# stack the outputs at each cell together so that we can conveniently compute loss and etc
s_mean_combined = tf.stack(s_mean_list)
s_std_combined = tf.stack(s_std_list)
s_logstd_combined = tf.stack(s_logstd_list)
final_state_combined = tf.stack(state)
return s_mean_combined, s_std_combined, s_logstd_combined, final_state_combined
def _init(self, ob_space, ac_space, hid_size, num_hid_layers, kind):
print type(ob_space)
assert isinstance(ob_space, gym.spaces.box.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob",
dtype=tf.float32,
shape=[sequence_length] + list(ob_space.shape))
self.ob = [ob]
#process ob_
x = ob / 255.0
ob_last = self.img_encoder(x, kind)
with tf.variable_scope("vf"):
last_out = ob_last
for i in range(num_hid_layers):
last_out = tf.nn.relu(
tf.layers.dense(
last_out,
hid_size,
name="fc%i" % (i + 1),
kernel_initializer=U.normc_initializer(1.0)))
self.vpred = tf.layers.dense(
last_out,
1,
name='final',
kernel_initializer=U.normc_initializer(1.0))[:, 0]
with tf.variable_scope("pol"):
last_out = ob_last
for i in range(num_hid_layers):
last_out = tf.nn.tanh(
tf.layers.dense(
last_out,
hid_size,
name='fc%i' % (i + 1),
kernel_initializer=U.normc_initializer(1.0)))
logits = tf.layers.dense(
last_out,
pdtype.param_shape()[0],
name='logits',
kernel_initializer=U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(logits)
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode()) # XXX
self._act = U.function([stochastic, ob], [ac, self.vpred])
def __init__(self,
name,
observation_shape,
action_shape,
hid_size,
num_hid_layers,
stochastic=True):
with tf.variable_scope(name):
self.stochastic = stochastic
self.hid_size, self.num_hid_layers = hid_size, num_hid_layers
self.action_shape, self.observation_shape = action_shape, observation_shape
self.scope = tf.get_variable_scope().name
self.pdtype = DiagGaussianPdType(action_shape[0])
observations_ph = U.get_placeholder(name='ob',
dtype=tf.float32,
shape=[None] +
list(observation_shape))
stochastic_ph = tf.placeholder(dtype=tf.bool, shape=())
with tf.variable_scope('obfilter'):
self.ob_rms = RunningMeanStd(shape=observation_shape)
with tf.variable_scope('pol'):
last_out = tf.clip_by_value(
(observations_ph - self.ob_rms.mean) / self.ob_rms.std,
-5.0, 5.0)
for i in range(num_hid_layers):
last_out = tf.nn.tanh(
tf.layers.dense(
last_out,
hid_size,
name='fc%i' % (i + 1),
kernel_initializer=U.normc_initializer(1.0)))
mean = tf.layers.dense(
last_out,
self.pdtype.param_shape()[0] // 2,
name='final',
kernel_initializer=U.normc_initializer(0.01))
logstd = tf.get_variable(
name='logstd',
shape=[1, self.pdtype.param_shape()[0] // 2],
initializer=tf.zeros_initializer())
pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
self.pd = self.pdtype.pdfromflat(pdparam)
action_op = U.switch(stochastic_ph, self.pd.sample(),
self.pd.mode())
self._act = U.function([stochastic_ph, observations_ph], action_op)
def _init(self, ob_space, ac_space, hid_size, num_hid_layers, activation='tanh', gaussian_fixed_var=True, keep=1.0):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob_shape = OBSERVATION_DIM if PREPROCESS else ob_space.shape[0]
ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length, ob_shape])
if activation == 'tanh':
activ = tf.nn.tanh
elif activation == 'elu':
activ = tf.nn.elu
elif activation == 'lrelu':
activ = lambda x: tf.maximum(x, 0.01 * x)
else:
raise NotImplementedError("Not available activation: " + activation)
if PREPROCESS:
last_out = ob
else:
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape=ob_space.shape)
obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
last_out = obz
for i in range(num_hid_layers):
last_out = activ(U.dense(last_out, hid_size, "vffc%i" % (i + 1), weight_init=U.normc_initializer(1.0)))
last_out = tf.nn.dropout(last_out, keep_prob=keep, name="vdrop%i" % (i + 1))
self.vpred = U.dense(last_out, 1, "vffinal", weight_init=U.normc_initializer(1.0))[:, 0]
last_out = ob
for i in range(num_hid_layers):
last_out = activ(U.dense(last_out, hid_size, "polfc%i" % (i + 1), weight_init=U.normc_initializer(1.0)))
last_out = tf.nn.dropout(last_out, keep_prob=keep, name="pdrop%i" % (i + 1))
if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
mean = U.dense(last_out, pdtype.param_shape()[0] // 2, "polfinal", U.normc_initializer(0.01))
logstd = tf.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0] // 2],
initializer=tf.zeros_initializer())
pdparam = U.concatenate([mean, mean * 0.0 + logstd], axis=1)
else:
pdparam = U.dense(last_out, pdtype.param_shape()[0], "polfinal", U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self._act = U.function([stochastic, ob], [ac, self.vpred])
def __init__(self,
dim_state: int,
dim_action: int,
hidden_sizes: List[int],
normalizer: GaussianNormalizer,
init_std=1.):
super().__init__()
self.dim_state = dim_state
self.dim_action = dim_action
self.hidden_sizes = hidden_sizes
self.init_std = init_std
self.normalizer = normalizer
with self.scope:
self.op_states = tf.placeholder(tf.float32,
shape=[None, dim_state],
name='states')
self.op_actions_ = tf.placeholder(tf.float32,
shape=[None, dim_action],
name='actions')
layers = []
# note that the placeholder has size 105.
all_sizes = [dim_state, *self.hidden_sizes]
for i, (in_features, out_features) in enumerate(
zip(all_sizes[:-1], all_sizes[1:])):
layers.append(
nn.Linear(in_features,
out_features,
weight_initializer=normc_initializer(1)))
layers.append(nn.Tanh())
layers.append(
nn.Linear(all_sizes[-1],
dim_action,
weight_initializer=normc_initializer(0.01)))
self.net = nn.Sequential(*layers)
self.op_log_std = nn.Parameter(tf.constant(np.log(self.init_std),
shape=[self.dim_action],
dtype=tf.float32),
name='log_std')
self.distribution = self(self.op_states)
self.op_actions = self.distribution.sample()
self.op_actions_mean = self.distribution.mean()
self.op_actions_std = self.distribution.stddev()
self.op_nlls_ = -self.distribution.log_prob(
self.op_actions_).reduce_sum(axis=1)
self.register_callable('[states] => [actions]', self.fast)
def __init__(self, ob_dim, ac_dim): #pylint: disable=W0613
X = tf.placeholder(tf.float32, shape=[None, ob_dim*2+ac_dim*2+2]) # batch of observations
vtarg_n = tf.placeholder(tf.float32, shape=[None], name='vtarg')
wd_dict = {}
h1 = tf.nn.elu(dense(X, 64, "h1", weight_init=U.normc_initializer(1.0), bias_init=0, weight_loss_dict=wd_dict))
h2 = tf.nn.elu(dense(h1, 64, "h2", weight_init=U.normc_initializer(1.0), bias_init=0, weight_loss_dict=wd_dict))
vpred_n = dense(h2, 1, "hfinal", weight_init=U.normc_initializer(1.0), bias_init=0, weight_loss_dict=wd_dict)[:,0]
sample_vpred_n = vpred_n + tf.random_normal(tf.shape(vpred_n))
wd_loss = tf.get_collection("vf_losses", None)
loss = U.mean(tf.square(vpred_n - vtarg_n)) + tf.add_n(wd_loss)
loss_sampled = U.mean(tf.square(vpred_n - tf.stop_gradient(sample_vpred_n)))
self._predict = U.function([X], vpred_n)
optim = kfac.KfacOptimizer(learning_rate=0.001, cold_lr=0.001*(1-0.9), momentum=0.9, \
clip_kl=0.3, epsilon=0.1, stats_decay=0.95, \
async=1, kfac_update=2, cold_iter=50, \
weight_decay_dict=wd_dict, max_grad_norm=None)
vf_var_list = []
for var in tf.trainable_variables():
if "vf" in var.name:
vf_var_list.append(var)
update_op, self.q_runner = optim.minimize(loss, loss_sampled, var_list=vf_var_list)
self.do_update = U.function([X, vtarg_n], update_op) #pylint: disable=E1101
U.initialize() # Initialize uninitialized TF variables