def _build_solvers(self, json_data):
actor_stepsize = 0.001 if (self.ACTOR_STEPSIZE_KEY not in json_data
) else json_data[self.ACTOR_STEPSIZE_KEY]
actor_momentum = 0.9 if (self.ACTOR_MOMENTUM_KEY not in json_data
) else json_data[self.ACTOR_MOMENTUM_KEY]
critic_stepsize = 0.01 if (self.CRITIC_STEPSIZE_KEY not in json_data
) else json_data[self.CRITIC_STEPSIZE_KEY]
critic_momentum = 0.9 if (self.CRITIC_MOMENTUM_KEY not in json_data
) else json_data[self.CRITIC_MOMENTUM_KEY]
critic_vars = self._tf_vars('main/critic')
critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize,
momentum=critic_momentum)
self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars)
self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars)
self._actor_stepsize_tf = tf.get_variable(dtype=tf.float32,
name='actor_stepsize',
initializer=actor_stepsize,
trainable=False)
self._actor_stepsize_ph = tf.get_variable(dtype=tf.float32,
name='actor_stepsize_ph',
shape=[])
self._actor_stepsize_update_op = self._actor_stepsize_tf.assign(
self._actor_stepsize_ph)
actor_vars = self._tf_vars('main/actor')
actor_opt = tf.train.MomentumOptimizer(
learning_rate=self._actor_stepsize_tf, momentum=actor_momentum)
self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars)
self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars)
return
def _build_solvers(self, json_data):
actor_stepsize = 0.001 if (
self.ACTOR_STEPSIZE_KEY not in json_data) else json_data[self.ACTOR_STEPSIZE_KEY]
actor_momentum = 0.9 if (
self.ACTOR_MOMENTUM_KEY not in json_data) else json_data[self.ACTOR_MOMENTUM_KEY]
critic_stepsize = 0.01 if (
self.CRITIC_STEPSIZE_KEY not in json_data) else json_data[self.CRITIC_STEPSIZE_KEY]
critic_momentum = 0.9 if (
self.CRITIC_MOMENTUM_KEY not in json_data) else json_data[self.CRITIC_MOMENTUM_KEY]
critic_vars = self._tf_vars('main/critic')
critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize,
momentum=critic_momentum)
self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars)
self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars)
self._actor_stepsize_tf = tf.get_variable(dtype=tf.float32,
name='actor_stepsize',
initializer=actor_stepsize,
trainable=False)
self._actor_stepsize_ph = tf.get_variable(dtype=tf.float32, name='actor_stepsize_ph', shape=[])
self._actor_stepsize_update_op = self._actor_stepsize_tf.assign(self._actor_stepsize_ph)
actor_vars = self._tf_vars('main/actor')
actor_opt = tf.train.MomentumOptimizer(learning_rate=self._actor_stepsize_tf,
momentum=actor_momentum)
self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars)
self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars)
return
def _build_solvers(self, json_data):
actor_stepsize = 0.001 if (self.ACTOR_STEPSIZE_KEY not in json_data) else json_data[self.ACTOR_STEPSIZE_KEY]
actor_momentum = 0.9 if (self.ACTOR_MOMENTUM_KEY not in json_data) else json_data[self.ACTOR_MOMENTUM_KEY]
critic_stepsize = 0.01 if (self.CRITIC_STEPSIZE_KEY not in json_data) else json_data[self.CRITIC_STEPSIZE_KEY]
critic_momentum = 0.9 if (self.CRITIC_MOMENTUM_KEY not in json_data) else json_data[self.CRITIC_MOMENTUM_KEY]
critic_vars = self._tf_vars('main/critic')
critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize, momentum=critic_momentum)
self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars)
self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars)
actor_vars = self._tf_vars('main/actor')
actor_opt = tf.train.MomentumOptimizer(learning_rate=actor_stepsize, momentum=actor_momentum)
self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars)
self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars)
return
def _build_solvers(self, json_data):
actor_stepsize = 0.001 if (
self.ACTOR_STEPSIZE_KEY not in json_data) else json_data[self.ACTOR_STEPSIZE_KEY]
actor_momentum = 0.9 if (
self.ACTOR_MOMENTUM_KEY not in json_data) else json_data[self.ACTOR_MOMENTUM_KEY]
critic_stepsize = 0.01 if (
self.CRITIC_STEPSIZE_KEY not in json_data) else json_data[self.CRITIC_STEPSIZE_KEY]
critic_momentum = 0.9 if (
self.CRITIC_MOMENTUM_KEY not in json_data) else json_data[self.CRITIC_MOMENTUM_KEY]
critic_vars = self._tf_vars('main/critic')
critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize,
momentum=critic_momentum)
self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars)
self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars)
actor_vars = self._tf_vars('main/actor')
actor_opt = tf.train.MomentumOptimizer(learning_rate=actor_stepsize, momentum=actor_momentum)
self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars)
self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars)
return
class PGAgent(TFAgent):
NAME = 'PG'
ACTOR_NET_KEY = 'ActorNet'
ACTOR_STEPSIZE_KEY = 'ActorStepsize'
ACTOR_MOMENTUM_KEY = 'ActorMomentum'
ACTOR_WEIGHT_DECAY_KEY = 'ActorWeightDecay'
ACTOR_INIT_OUTPUT_SCALE_KEY = 'ActorInitOutputScale'
CRITIC_NET_KEY = 'CriticNet'
CRITIC_STEPSIZE_KEY = 'CriticStepsize'
CRITIC_MOMENTUM_KEY = 'CriticMomentum'
CRITIC_WEIGHT_DECAY_KEY = 'CriticWeightDecay'
EXP_ACTION_FLAG = 1 << 0
def __init__(self, world, id, json_data):
self._exp_action = False
super().__init__(world, id, json_data)
return
def reset(self):
super().reset()
self._exp_action = False
return
def _check_action_space(self):
action_space = self.get_action_space()
return action_space == ActionSpace.Continuous
def _load_params(self, json_data):
super()._load_params(json_data)
self.val_min, self.val_max = self._calc_val_bounds(self.discount)
self.val_fail, self.val_succ = self._calc_term_vals(self.discount)
return
def _build_nets(self, json_data):
assert self.ACTOR_NET_KEY in json_data
assert self.CRITIC_NET_KEY in json_data
actor_net_name = json_data[self.ACTOR_NET_KEY]
critic_net_name = json_data[self.CRITIC_NET_KEY]
actor_init_output_scale = 1 if (self.ACTOR_INIT_OUTPUT_SCALE_KEY not in json_data
) else json_data[self.ACTOR_INIT_OUTPUT_SCALE_KEY]
s_size = self.get_state_size()
g_size = self.get_goal_size()
a_size = self.get_action_size()
# setup input tensors
self.s_tf = tf.placeholder(tf.float32, shape=[None, s_size], name="s") # observations
self.tar_val_tf = tf.placeholder(tf.float32, shape=[None], name="tar_val") # target value s
self.adv_tf = tf.placeholder(tf.float32, shape=[None], name="adv") # advantage
self.a_tf = tf.placeholder(tf.float32, shape=[None, a_size], name="a") # target actions
self.g_tf = tf.placeholder(tf.float32,
shape=([None, g_size] if self.has_goal() else None),
name="g") # goals
with tf.variable_scope('main'):
with tf.variable_scope('actor'):
self.actor_tf = self._build_net_actor(actor_net_name, actor_init_output_scale)
with tf.variable_scope('critic'):
self.critic_tf = self._build_net_critic(critic_net_name)
if (self.actor_tf != None):
Logger.print2('Built actor net: ' + actor_net_name)
if (self.critic_tf != None):
Logger.print2('Built critic net: ' + critic_net_name)
return
def _build_normalizers(self):
super()._build_normalizers()
with self.sess.as_default(), self.graph.as_default(), tf.variable_scope(self.tf_scope):
with tf.variable_scope(self.RESOURCE_SCOPE):
val_offset, val_scale = self._calc_val_offset_scale(self.discount)
self.val_norm = TFNormalizer(self.sess, 'val_norm', 1)
self.val_norm.set_mean_std(-val_offset, 1.0 / val_scale)
return
def _init_normalizers(self):
super()._init_normalizers()
with self.sess.as_default(), self.graph.as_default():
self.val_norm.update()
return
def _load_normalizers(self):
super()._load_normalizers()
self.val_norm.load()
return
def _build_losses(self, json_data):
actor_weight_decay = 0 if (
self.ACTOR_WEIGHT_DECAY_KEY not in json_data) else json_data[self.ACTOR_WEIGHT_DECAY_KEY]
critic_weight_decay = 0 if (
self.CRITIC_WEIGHT_DECAY_KEY not in json_data) else json_data[self.CRITIC_WEIGHT_DECAY_KEY]
norm_val_diff = self.val_norm.normalize_tf(self.tar_val_tf) - self.val_norm.normalize_tf(
self.critic_tf)
self.critic_loss_tf = 0.5 * tf.reduce_mean(tf.square(norm_val_diff))
if (critic_weight_decay != 0):
self.critic_loss_tf += critic_weight_decay * self._weight_decay_loss('main/critic')
norm_a_mean_tf = self.a_norm.normalize_tf(self.actor_tf)
norm_a_diff = self.a_norm.normalize_tf(self.a_tf) - norm_a_mean_tf
self.actor_loss_tf = tf.reduce_sum(tf.square(norm_a_diff), axis=-1)
self.actor_loss_tf *= self.adv_tf
self.actor_loss_tf = 0.5 * tf.reduce_mean(self.actor_loss_tf)
norm_a_bound_min = self.a_norm.normalize(self.a_bound_min)
norm_a_bound_max = self.a_norm.normalize(self.a_bound_max)
a_bound_loss = TFUtil.calc_bound_loss(norm_a_mean_tf, norm_a_bound_min, norm_a_bound_max)
a_bound_loss /= self.exp_params_curr.noise
self.actor_loss_tf += a_bound_loss
if (actor_weight_decay != 0):
self.actor_loss_tf += actor_weight_decay * self._weight_decay_loss('main/actor')
return
def _build_solvers(self, json_data):
actor_stepsize = 0.001 if (
self.ACTOR_STEPSIZE_KEY not in json_data) else json_data[self.ACTOR_STEPSIZE_KEY]
actor_momentum = 0.9 if (
self.ACTOR_MOMENTUM_KEY not in json_data) else json_data[self.ACTOR_MOMENTUM_KEY]
critic_stepsize = 0.01 if (
self.CRITIC_STEPSIZE_KEY not in json_data) else json_data[self.CRITIC_STEPSIZE_KEY]
critic_momentum = 0.9 if (
self.CRITIC_MOMENTUM_KEY not in json_data) else json_data[self.CRITIC_MOMENTUM_KEY]
critic_vars = self._tf_vars('main/critic')
critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize,
momentum=critic_momentum)
self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars)
self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars)
actor_vars = self._tf_vars('main/actor')
actor_opt = tf.train.MomentumOptimizer(learning_rate=actor_stepsize, momentum=actor_momentum)
self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars)
self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars)
return
def _build_net_actor(self, net_name, init_output_scale):
norm_s_tf = self.s_norm.normalize_tf(self.s_tf)
input_tfs = [norm_s_tf]
if (self.has_goal()):
norm_g_tf = self.g_norm.normalize_tf(self.g_tf)
input_tfs += [norm_g_tf]
h = NetBuilder.build_net(net_name, input_tfs)
norm_a_tf = tf.layers.dense(inputs=h,
units=self.get_action_size(),
activation=None,
kernel_initializer=tf.random_uniform_initializer(
minval=-init_output_scale, maxval=init_output_scale))
a_tf = self.a_norm.unnormalize_tf(norm_a_tf)
return a_tf
def _build_net_critic(self, net_name):
norm_s_tf = self.s_norm.normalize_tf(self.s_tf)
input_tfs = [norm_s_tf]
if (self.has_goal()):
norm_g_tf = self.g_norm.normalize_tf(self.g_tf)
input_tfs += [norm_g_tf]
h = NetBuilder.build_net(net_name, input_tfs)
norm_val_tf = tf.layers.dense(inputs=h,
units=1,
activation=None,
kernel_initializer=TFUtil.xavier_initializer)
norm_val_tf = tf.reshape(norm_val_tf, [-1])
val_tf = self.val_norm.unnormalize_tf(norm_val_tf)
return val_tf
def _initialize_vars(self):
super()._initialize_vars()
self._sync_solvers()
return
def _sync_solvers(self):
self.actor_solver.sync()
self.critic_solver.sync()
return
def _decide_action(self, s, g):
with self.sess.as_default(), self.graph.as_default():
self._exp_action = False
a = self._eval_actor(s, g)[0]
logp = 0
if self._enable_stoch_policy():
# epsilon-greedy
rand_action = MathUtil.flip_coin(self.exp_params_curr.rate)
if rand_action:
norm_exp_noise = np.random.randn(*a.shape)
norm_exp_noise *= self.exp_params_curr.noise
exp_noise = norm_exp_noise * self.a_norm.std
a += exp_noise
logp = self._calc_action_logp(norm_exp_noise)
self._exp_action = True
return a, logp
def _enable_stoch_policy(self):
return self.enable_training and (self._mode == self.Mode.TRAIN or
self._mode == self.Mode.TRAIN_END)
def _eval_actor(self, s, g):
s = np.reshape(s, [-1, self.get_state_size()])
g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None
feed = {self.s_tf: s, self.g_tf: g}
a = self.actor_tf.eval(feed)
return a
def _eval_critic(self, s, g):
with self.sess.as_default(), self.graph.as_default():
s = np.reshape(s, [-1, self.get_state_size()])
g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None
feed = {self.s_tf: s, self.g_tf: g}
val = self.critic_tf.eval(feed)
return val
def _record_flags(self):
flags = int(0)
if (self._exp_action):
flags = flags | self.EXP_ACTION_FLAG
return flags
def _train_step(self):
super()._train_step()
critic_loss = self._update_critic()
actor_loss = self._update_actor()
critic_loss = MPIUtil.reduce_avg(critic_loss)
actor_loss = MPIUtil.reduce_avg(actor_loss)
critic_stepsize = self.critic_solver.get_stepsize()
actor_stepsize = self.actor_solver.get_stepsize()
self.logger.log_tabular('Critic_Loss', critic_loss)
self.logger.log_tabular('Critic_Stepsize', critic_stepsize)
self.logger.log_tabular('Actor_Loss', actor_loss)
self.logger.log_tabular('Actor_Stepsize', actor_stepsize)
return
def _update_critic(self):
idx = self.replay_buffer.sample(self._local_mini_batch_size)
s = self.replay_buffer.get('states', idx)
g = self.replay_buffer.get('goals', idx) if self.has_goal() else None
tar_V = self._calc_updated_vals(idx)
tar_V = np.clip(tar_V, self.val_min, self.val_max)
feed = {self.s_tf: s, self.g_tf: g, self.tar_val_tf: tar_V}
loss, grads = self.sess.run([self.critic_loss_tf, self.critic_grad_tf], feed)
self.critic_solver.update(grads)
return loss
def _update_actor(self):
key = self.EXP_ACTION_FLAG
idx = self.replay_buffer.sample_filtered(self._local_mini_batch_size, key)
has_goal = self.has_goal()
s = self.replay_buffer.get('states', idx)
g = self.replay_buffer.get('goals', idx) if has_goal else None
a = self.replay_buffer.get('actions', idx)
V_new = self._calc_updated_vals(idx)
V_old = self._eval_critic(s, g)
adv = V_new - V_old
feed = {self.s_tf: s, self.g_tf: g, self.a_tf: a, self.adv_tf: adv}
loss, grads = self.sess.run([self.actor_loss_tf, self.actor_grad_tf], feed)
self.actor_solver.update(grads)
return loss
def _calc_updated_vals(self, idx):
r = self.replay_buffer.get('rewards', idx)
if self.discount == 0:
new_V = r
else:
next_idx = self.replay_buffer.get_next_idx(idx)
s_next = self.replay_buffer.get('states', next_idx)
g_next = self.replay_buffer.get('goals', next_idx) if self.has_goal() else None
is_end = self.replay_buffer.is_path_end(idx)
is_fail = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Fail)
is_succ = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Succ)
is_fail = np.logical_and(is_end, is_fail)
is_succ = np.logical_and(is_end, is_succ)
V_next = self._eval_critic(s_next, g_next)
V_next[is_fail] = self.val_fail
V_next[is_succ] = self.val_succ
new_V = r + self.discount * V_next
return new_V
def _calc_action_logp(self, norm_action_deltas):
# norm action delta are for the normalized actions (scaled by self.a_norm.std)
stdev = self.exp_params_curr.noise
assert stdev > 0
a_size = self.get_action_size()
logp = -0.5 / (stdev * stdev) * np.sum(np.square(norm_action_deltas), axis=-1)
logp += -0.5 * a_size * np.log(2 * np.pi)
logp += -a_size * np.log(stdev)
return logp
def _log_val(self, s, g):
val = self._eval_critic(s, g)
norm_val = self.val_norm.normalize(val)
self.world.env.log_val(self.id, norm_val[0])
return
def _build_replay_buffer(self, buffer_size):
super()._build_replay_buffer(buffer_size)
self.replay_buffer.add_filter_key(self.EXP_ACTION_FLAG)
return
class PGAgent(TFAgent):
NAME = 'PG'
ACTOR_NET_KEY = 'ActorNet'
ACTOR_STEPSIZE_KEY = 'ActorStepsize'
ACTOR_MOMENTUM_KEY = 'ActorMomentum'
ACTOR_WEIGHT_DECAY_KEY = 'ActorWeightDecay'
ACTOR_INIT_OUTPUT_SCALE_KEY = 'ActorInitOutputScale'
CRITIC_NET_KEY = 'CriticNet'
CRITIC_STEPSIZE_KEY = 'CriticStepsize'
CRITIC_MOMENTUM_KEY = 'CriticMomentum'
CRITIC_WEIGHT_DECAY_KEY = 'CriticWeightDecay'
EXP_ACTION_FLAG = 1 << 0
def __init__(self, world, id, json_data):
self._exp_action = False
super().__init__(world, id, json_data)
return
def reset(self):
super().reset()
self._exp_action = False
return
def _check_action_space(self):
action_space = self.get_action_space()
return action_space == ActionSpace.Continuous
def _load_params(self, json_data):
super()._load_params(json_data)
self.val_min, self.val_max = self._calc_val_bounds(self.discount)
self.val_fail, self.val_succ = self._calc_term_vals(self.discount)
return
def _build_nets(self, json_data):
assert self.ACTOR_NET_KEY in json_data
assert self.CRITIC_NET_KEY in json_data
actor_net_name = json_data[self.ACTOR_NET_KEY]
critic_net_name = json_data[self.CRITIC_NET_KEY]
actor_init_output_scale = 1 if (self.ACTOR_INIT_OUTPUT_SCALE_KEY not in json_data
) else json_data[self.ACTOR_INIT_OUTPUT_SCALE_KEY]
s_size = self.get_state_size()
g_size = self.get_goal_size()
a_size = self.get_action_size()
# setup input tensors
self.s_tf = tf.placeholder(tf.float32, shape=[None, s_size], name="s") # observations
self.tar_val_tf = tf.placeholder(tf.float32, shape=[None], name="tar_val") # target value s
self.adv_tf = tf.placeholder(tf.float32, shape=[None], name="adv") # advantage
self.a_tf = tf.placeholder(tf.float32, shape=[None, a_size], name="a") # target actions
self.g_tf = tf.placeholder(tf.float32,
shape=([None, g_size] if self.has_goal() else None),
name="g") # goals
with tf.variable_scope('main'):
with tf.variable_scope('actor'):
self.actor_tf = self._build_net_actor(actor_net_name, actor_init_output_scale)
with tf.variable_scope('critic'):
self.critic_tf = self._build_net_critic(critic_net_name)
if (self.actor_tf != None):
Logger.print2('Built actor net: ' + actor_net_name)
if (self.critic_tf != None):
Logger.print2('Built critic net: ' + critic_net_name)
return
def _build_normalizers(self):
super()._build_normalizers()
with self.sess.as_default(), self.graph.as_default(), tf.variable_scope(self.tf_scope):
with tf.variable_scope(self.RESOURCE_SCOPE):
val_offset, val_scale = self._calc_val_offset_scale(self.discount)
self.val_norm = TFNormalizer(self.sess, 'val_norm', 1)
self.val_norm.set_mean_std(-val_offset, 1.0 / val_scale)
return
def _init_normalizers(self):
super()._init_normalizers()
with self.sess.as_default(), self.graph.as_default():
self.val_norm.update()
return
def _load_normalizers(self):
super()._load_normalizers()
self.val_norm.load()
return
def _build_losses(self, json_data):
actor_weight_decay = 0 if (
self.ACTOR_WEIGHT_DECAY_KEY not in json_data) else json_data[self.ACTOR_WEIGHT_DECAY_KEY]
critic_weight_decay = 0 if (
self.CRITIC_WEIGHT_DECAY_KEY not in json_data) else json_data[self.CRITIC_WEIGHT_DECAY_KEY]
norm_val_diff = self.val_norm.normalize_tf(self.tar_val_tf) - self.val_norm.normalize_tf(
self.critic_tf)
self.critic_loss_tf = 0.5 * tf.reduce_mean(tf.square(norm_val_diff))
if (critic_weight_decay != 0):
self.critic_loss_tf += critic_weight_decay * self._weight_decay_loss('main/critic')
norm_a_mean_tf = self.a_norm.normalize_tf(self.actor_tf)
norm_a_diff = self.a_norm.normalize_tf(self.a_tf) - norm_a_mean_tf
self.actor_loss_tf = tf.reduce_sum(tf.square(norm_a_diff), axis=-1)
self.actor_loss_tf *= self.adv_tf
self.actor_loss_tf = 0.5 * tf.reduce_mean(self.actor_loss_tf)
norm_a_bound_min = self.a_norm.normalize(self.a_bound_min)
norm_a_bound_max = self.a_norm.normalize(self.a_bound_max)
a_bound_loss = TFUtil.calc_bound_loss(norm_a_mean_tf, norm_a_bound_min, norm_a_bound_max)
a_bound_loss /= self.exp_params_curr.noise
self.actor_loss_tf += a_bound_loss
if (actor_weight_decay != 0):
self.actor_loss_tf += actor_weight_decay * self._weight_decay_loss('main/actor')
return
def _build_solvers(self, json_data):
actor_stepsize = 0.001 if (
self.ACTOR_STEPSIZE_KEY not in json_data) else json_data[self.ACTOR_STEPSIZE_KEY]
actor_momentum = 0.9 if (
self.ACTOR_MOMENTUM_KEY not in json_data) else json_data[self.ACTOR_MOMENTUM_KEY]
critic_stepsize = 0.01 if (
self.CRITIC_STEPSIZE_KEY not in json_data) else json_data[self.CRITIC_STEPSIZE_KEY]
critic_momentum = 0.9 if (
self.CRITIC_MOMENTUM_KEY not in json_data) else json_data[self.CRITIC_MOMENTUM_KEY]
critic_vars = self._tf_vars('main/critic')
critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize,
momentum=critic_momentum)
self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars)
self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars)
actor_vars = self._tf_vars('main/actor')
actor_opt = tf.train.MomentumOptimizer(learning_rate=actor_stepsize, momentum=actor_momentum)
self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars)
self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars)
return
def _build_net_actor(self, net_name, init_output_scale):
norm_s_tf = self.s_norm.normalize_tf(self.s_tf)
input_tfs = [norm_s_tf]
if (self.has_goal()):
norm_g_tf = self.g_norm.normalize_tf(self.g_tf)
input_tfs += [norm_g_tf]
h = NetBuilder.build_net(net_name, input_tfs)
norm_a_tf = tf.layers.dense(inputs=h,
units=self.get_action_size(),
activation=None,
kernel_initializer=tf.random_uniform_initializer(
minval=-init_output_scale, maxval=init_output_scale))
a_tf = self.a_norm.unnormalize_tf(norm_a_tf)
return a_tf
def _build_net_critic(self, net_name):
norm_s_tf = self.s_norm.normalize_tf(self.s_tf)
input_tfs = [norm_s_tf]
if (self.has_goal()):
norm_g_tf = self.g_norm.normalize_tf(self.g_tf)
input_tfs += [norm_g_tf]
h = NetBuilder.build_net(net_name, input_tfs)
norm_val_tf = tf.layers.dense(inputs=h,
units=1,
activation=None,
kernel_initializer=TFUtil.xavier_initializer)
norm_val_tf = tf.reshape(norm_val_tf, [-1])
val_tf = self.val_norm.unnormalize_tf(norm_val_tf)
return val_tf
def _initialize_vars(self):
super()._initialize_vars()
self._sync_solvers()
return
def _sync_solvers(self):
self.actor_solver.sync()
self.critic_solver.sync()
return
def _decide_action(self, s, g):
with self.sess.as_default(), self.graph.as_default():
self._exp_action = False
a = self._eval_actor(s, g)[0]
logp = 0
if self._enable_stoch_policy():
# epsilon-greedy
rand_action = MathUtil.flip_coin(self.exp_params_curr.rate)
if rand_action:
norm_exp_noise = np.random.randn(*a.shape)
norm_exp_noise *= self.exp_params_curr.noise
exp_noise = norm_exp_noise * self.a_norm.std
a += exp_noise
logp = self._calc_action_logp(norm_exp_noise)
self._exp_action = True
return a, logp
def _enable_stoch_policy(self):
return self.enable_training and (self._mode == self.Mode.TRAIN or
self._mode == self.Mode.TRAIN_END)
def _eval_actor(self, s, g):
s = np.reshape(s, [-1, self.get_state_size()])
g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None
feed = {self.s_tf: s, self.g_tf: g}
a = self.actor_tf.eval(feed)
return a
def _eval_critic(self, s, g):
with self.sess.as_default(), self.graph.as_default():
s = np.reshape(s, [-1, self.get_state_size()])
g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None
feed = {self.s_tf: s, self.g_tf: g}
val = self.critic_tf.eval(feed)
return val
def _record_flags(self):
flags = int(0)
if (self._exp_action):
flags = flags | self.EXP_ACTION_FLAG
return flags
def _train_step(self):
super()._train_step()
critic_loss = self._update_critic()
actor_loss = self._update_actor()
critic_loss = MPIUtil.reduce_avg(critic_loss)
actor_loss = MPIUtil.reduce_avg(actor_loss)
critic_stepsize = self.critic_solver.get_stepsize()
actor_stepsize = self.actor_solver.get_stepsize()
self.logger.log_tabular('Critic_Loss', critic_loss)
self.logger.log_tabular('Critic_Stepsize', critic_stepsize)
self.logger.log_tabular('Actor_Loss', actor_loss)
self.logger.log_tabular('Actor_Stepsize', actor_stepsize)
return
def _update_critic(self):
idx = self.replay_buffer.sample(self._local_mini_batch_size)
s = self.replay_buffer.get('states', idx)
g = self.replay_buffer.get('goals', idx) if self.has_goal() else None
tar_V = self._calc_updated_vals(idx)
tar_V = np.clip(tar_V, self.val_min, self.val_max)
feed = {self.s_tf: s, self.g_tf: g, self.tar_val_tf: tar_V}
loss, grads = self.sess.run([self.critic_loss_tf, self.critic_grad_tf], feed)
self.critic_solver.update(grads)
return loss
def _update_actor(self):
key = self.EXP_ACTION_FLAG
idx = self.replay_buffer.sample_filtered(self._local_mini_batch_size, key)
has_goal = self.has_goal()
s = self.replay_buffer.get('states', idx)
g = self.replay_buffer.get('goals', idx) if has_goal else None
a = self.replay_buffer.get('actions', idx)
V_new = self._calc_updated_vals(idx)
V_old = self._eval_critic(s, g)
adv = V_new - V_old
feed = {self.s_tf: s, self.g_tf: g, self.a_tf: a, self.adv_tf: adv}
loss, grads = self.sess.run([self.actor_loss_tf, self.actor_grad_tf], feed)
self.actor_solver.update(grads)
return loss
def _calc_updated_vals(self, idx):
r = self.replay_buffer.get('rewards', idx)
if self.discount == 0:
new_V = r
else:
next_idx = self.replay_buffer.get_next_idx(idx)
s_next = self.replay_buffer.get('states', next_idx)
g_next = self.replay_buffer.get('goals', next_idx) if self.has_goal() else None
is_end = self.replay_buffer.is_path_end(idx)
is_fail = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Fail)
is_succ = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Succ)
is_fail = np.logical_and(is_end, is_fail)
is_succ = np.logical_and(is_end, is_succ)
V_next = self._eval_critic(s_next, g_next)
V_next[is_fail] = self.val_fail
V_next[is_succ] = self.val_succ
new_V = r + self.discount * V_next
return new_V
def _calc_action_logp(self, norm_action_deltas):
# norm action delta are for the normalized actions (scaled by self.a_norm.std)
stdev = self.exp_params_curr.noise
assert stdev > 0
a_size = self.get_action_size()
logp = -0.5 / (stdev * stdev) * np.sum(np.square(norm_action_deltas), axis=-1)
logp += -0.5 * a_size * np.log(2 * np.pi)
logp += -a_size * np.log(stdev)
return logp
def _log_val(self, s, g):
val = self._eval_critic(s, g)
norm_val = self.val_norm.normalize(val)
self.world.env.log_val(self.id, norm_val[0])
return
def _build_replay_buffer(self, buffer_size):
super()._build_replay_buffer(buffer_size)
self.replay_buffer.add_filter_key(self.EXP_ACTION_FLAG)
return
class PPOAgent(PGAgent):
NAME = "PPO"
EPOCHS_KEY = "Epochs"
BATCH_SIZE_KEY = "BatchSize"
RATIO_CLIP_KEY = "RatioClip"
NORM_ADV_CLIP_KEY = "NormAdvClip"
TD_LAMBDA_KEY = "TDLambda"
TAR_CLIP_FRAC = "TarClipFrac"
ACTOR_STEPSIZE_DECAY = "ActorStepsizeDecay"
def __init__(self, world, id, json_data):
super().__init__(world, id, json_data)
return
def _load_params(self, json_data):
super()._load_params(json_data)
self.epochs = 1 if (self.EPOCHS_KEY
not in json_data) else json_data[self.EPOCHS_KEY]
self.batch_size = 1024 if (self.BATCH_SIZE_KEY not in json_data
) else json_data[self.BATCH_SIZE_KEY]
self.ratio_clip = 0.2 if (self.RATIO_CLIP_KEY not in json_data
) else json_data[self.RATIO_CLIP_KEY]
self.norm_adv_clip = 5 if (self.NORM_ADV_CLIP_KEY not in json_data
) else json_data[self.NORM_ADV_CLIP_KEY]
self.td_lambda = 0.95 if (self.TD_LAMBDA_KEY not in json_data
) else json_data[self.TD_LAMBDA_KEY]
self.tar_clip_frac = -1 if (self.TAR_CLIP_FRAC not in json_data
) else json_data[self.TAR_CLIP_FRAC]
self.actor_stepsize_decay = 0.5 if (
self.ACTOR_STEPSIZE_DECAY
not in json_data) else json_data[self.ACTOR_STEPSIZE_DECAY]
num_procs = MPIUtil.get_num_procs()
local_batch_size = int(self.batch_size / num_procs)
min_replay_size = 2 * local_batch_size # needed to prevent buffer overflow
assert (self.replay_buffer_size > min_replay_size)
self.replay_buffer_size = np.maximum(min_replay_size,
self.replay_buffer_size)
return
def _build_nets(self, json_data):
assert self.ACTOR_NET_KEY in json_data
assert self.CRITIC_NET_KEY in json_data
actor_net_name = json_data[self.ACTOR_NET_KEY]
critic_net_name = json_data[self.CRITIC_NET_KEY]
actor_init_output_scale = 1 if (
self.ACTOR_INIT_OUTPUT_SCALE_KEY
not in json_data) else json_data[self.ACTOR_INIT_OUTPUT_SCALE_KEY]
s_size = self.get_state_size()
g_size = self.get_goal_size()
a_size = self.get_action_size()
# setup input tensors
self.s_tf = tf.placeholder(tf.float32, shape=[None, s_size], name="s")
self.a_tf = tf.placeholder(tf.float32, shape=[None, a_size], name="a")
self.tar_val_tf = tf.placeholder(tf.float32,
shape=[None],
name="tar_val")
self.adv_tf = tf.placeholder(tf.float32, shape=[None], name="adv")
self.g_tf = tf.placeholder(
tf.float32,
shape=([None, g_size] if self.has_goal() else None),
name="g")
self.old_logp_tf = tf.placeholder(tf.float32,
shape=[None],
name="old_logp")
self.exp_mask_tf = tf.placeholder(tf.float32,
shape=[None],
name="exp_mask")
with tf.variable_scope('main'):
with tf.variable_scope('actor'):
self.a_mean_tf = self._build_net_actor(
actor_net_name, actor_init_output_scale)
with tf.variable_scope('critic'):
self.critic_tf = self._build_net_critic(critic_net_name)
if (self.a_mean_tf != None):
Logger.print2('Built actor net: ' + actor_net_name)
if (self.critic_tf != None):
Logger.print2('Built critic net: ' + critic_net_name)
self.norm_a_std_tf = self.exp_params_curr.noise * tf.ones(a_size)
norm_a_noise_tf = self.norm_a_std_tf * tf.random_normal(
shape=tf.shape(self.a_mean_tf))
norm_a_noise_tf *= tf.expand_dims(self.exp_mask_tf, axis=-1)
self.sample_a_tf = self.a_mean_tf + norm_a_noise_tf * self.a_norm.std_tf
self.sample_a_logp_tf = TFUtil.calc_logp_gaussian(
x_tf=norm_a_noise_tf, mean_tf=None, std_tf=self.norm_a_std_tf)
return
def _build_losses(self, json_data):
actor_weight_decay = 0 if (
self.ACTOR_WEIGHT_DECAY_KEY
not in json_data) else json_data[self.ACTOR_WEIGHT_DECAY_KEY]
critic_weight_decay = 0 if (
self.CRITIC_WEIGHT_DECAY_KEY
not in json_data) else json_data[self.CRITIC_WEIGHT_DECAY_KEY]
norm_val_diff = self.val_norm.normalize_tf(
self.tar_val_tf) - self.val_norm.normalize_tf(self.critic_tf)
self.critic_loss_tf = 0.5 * tf.reduce_mean(tf.square(norm_val_diff))
if (critic_weight_decay != 0):
self.critic_loss_tf += critic_weight_decay * self._weight_decay_loss(
'main/critic')
norm_tar_a_tf = self.a_norm.normalize_tf(self.a_tf)
self._norm_a_mean_tf = self.a_norm.normalize_tf(self.a_mean_tf)
self.logp_tf = TFUtil.calc_logp_gaussian(norm_tar_a_tf,
self._norm_a_mean_tf,
self.norm_a_std_tf)
ratio_tf = tf.exp(self.logp_tf - self.old_logp_tf)
actor_loss0 = self.adv_tf * ratio_tf
actor_loss1 = self.adv_tf * tf.clip_by_value(
ratio_tf, 1.0 - self.ratio_clip, 1 + self.ratio_clip)
self.actor_loss_tf = -tf.reduce_mean(
tf.minimum(actor_loss0, actor_loss1))
norm_a_bound_min = self.a_norm.normalize(self.a_bound_min)
norm_a_bound_max = self.a_norm.normalize(self.a_bound_max)
a_bound_loss = TFUtil.calc_bound_loss(self._norm_a_mean_tf,
norm_a_bound_min,
norm_a_bound_max)
self.actor_loss_tf += a_bound_loss
if (actor_weight_decay != 0):
self.actor_loss_tf += actor_weight_decay * self._weight_decay_loss(
'main/actor')
# for debugging
self.clip_frac_tf = tf.reduce_mean(
tf.to_float(tf.greater(tf.abs(ratio_tf - 1.0), self.ratio_clip)))
return
def _build_solvers(self, json_data):
actor_stepsize = 0.001 if (self.ACTOR_STEPSIZE_KEY not in json_data
) else json_data[self.ACTOR_STEPSIZE_KEY]
actor_momentum = 0.9 if (self.ACTOR_MOMENTUM_KEY not in json_data
) else json_data[self.ACTOR_MOMENTUM_KEY]
critic_stepsize = 0.01 if (self.CRITIC_STEPSIZE_KEY not in json_data
) else json_data[self.CRITIC_STEPSIZE_KEY]
critic_momentum = 0.9 if (self.CRITIC_MOMENTUM_KEY not in json_data
) else json_data[self.CRITIC_MOMENTUM_KEY]
critic_vars = self._tf_vars('main/critic')
critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize,
momentum=critic_momentum)
self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars)
self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars)
self._actor_stepsize_tf = tf.get_variable(dtype=tf.float32,
name='actor_stepsize',
initializer=actor_stepsize,
trainable=False)
self._actor_stepsize_ph = tf.get_variable(dtype=tf.float32,
name='actor_stepsize_ph',
shape=[])
self._actor_stepsize_update_op = self._actor_stepsize_tf.assign(
self._actor_stepsize_ph)
actor_vars = self._tf_vars('main/actor')
actor_opt = tf.train.MomentumOptimizer(
learning_rate=self._actor_stepsize_tf, momentum=actor_momentum)
self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars)
self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars)
return
def _decide_action(self, s, g):
with self.sess.as_default(), self.graph.as_default():
self._exp_action = self._enable_stoch_policy(
) and MathUtil.flip_coin(self.exp_params_curr.rate)
#print("_decide_action._exp_action=",self._exp_action)
a, logp = self._eval_actor(s, g, self._exp_action)
return a[0], logp[0]
def _eval_actor(self, s, g, enable_exp):
s = np.reshape(s, [-1, self.get_state_size()])
g = np.reshape(g,
[-1, self.get_goal_size()]) if self.has_goal() else None
feed = {
self.s_tf: s,
self.g_tf: g,
self.exp_mask_tf: np.array([1 if enable_exp else 0])
}
a, logp = self.sess.run([self.sample_a_tf, self.sample_a_logp_tf],
feed_dict=feed)
return a, logp
def _train_step(self):
adv_eps = 1e-5
start_idx = self.replay_buffer.buffer_tail
end_idx = self.replay_buffer.buffer_head
assert (start_idx == 0)
assert (self.replay_buffer.get_current_size() <=
self.replay_buffer.buffer_size) # must avoid overflow
assert (start_idx < end_idx)
idx = np.array(list(range(start_idx, end_idx)))
end_mask = self.replay_buffer.is_path_end(idx)
end_mask = np.logical_not(end_mask)
vals = self._compute_batch_vals(start_idx, end_idx)
new_vals = self._compute_batch_new_vals(start_idx, end_idx, vals)
valid_idx = idx[end_mask]
exp_idx = self.replay_buffer.get_idx_filtered(
self.EXP_ACTION_FLAG).copy()
num_valid_idx = valid_idx.shape[0]
num_exp_idx = exp_idx.shape[0]
exp_idx = np.column_stack(
[exp_idx,
np.array(list(range(0, num_exp_idx)), dtype=np.int32)])
local_sample_count = valid_idx.size
global_sample_count = int(MPIUtil.reduce_sum(local_sample_count))
mini_batches = int(np.ceil(global_sample_count / self.mini_batch_size))
adv = new_vals[exp_idx[:, 0]] - vals[exp_idx[:, 0]]
new_vals = np.clip(new_vals, self.val_min, self.val_max)
adv_mean = np.mean(adv)
adv_std = np.std(adv)
adv = (adv - adv_mean) / (adv_std + adv_eps)
adv = np.clip(adv, -self.norm_adv_clip, self.norm_adv_clip)
critic_loss = 0
actor_loss = 0
actor_clip_frac = 0
for e in range(self.epochs):
np.random.shuffle(valid_idx)
np.random.shuffle(exp_idx)
for b in range(mini_batches):
batch_idx_beg = b * self._local_mini_batch_size
batch_idx_end = batch_idx_beg + self._local_mini_batch_size
critic_batch = np.array(range(batch_idx_beg, batch_idx_end),
dtype=np.int32)
actor_batch = critic_batch.copy()
critic_batch = np.mod(critic_batch, num_valid_idx)
actor_batch = np.mod(actor_batch, num_exp_idx)
shuffle_actor = (actor_batch[-1] < actor_batch[0]) or (
actor_batch[-1] == num_exp_idx - 1)
critic_batch = valid_idx[critic_batch]
actor_batch = exp_idx[actor_batch]
critic_batch_vals = new_vals[critic_batch]
actor_batch_adv = adv[actor_batch[:, 1]]
critic_s = self.replay_buffer.get('states', critic_batch)
critic_g = self.replay_buffer.get(
'goals', critic_batch) if self.has_goal() else None
curr_critic_loss = self._update_critic(critic_s, critic_g,
critic_batch_vals)
actor_s = self.replay_buffer.get("states", actor_batch[:, 0])
actor_g = self.replay_buffer.get(
"goals", actor_batch[:, 0]) if self.has_goal() else None
actor_a = self.replay_buffer.get("actions", actor_batch[:, 0])
actor_logp = self.replay_buffer.get("logps", actor_batch[:, 0])
curr_actor_loss, curr_actor_clip_frac = self._update_actor(
actor_s, actor_g, actor_a, actor_logp, actor_batch_adv)
critic_loss += curr_critic_loss
actor_loss += np.abs(curr_actor_loss)
actor_clip_frac += curr_actor_clip_frac
if (shuffle_actor):
np.random.shuffle(exp_idx)
total_batches = mini_batches * self.epochs
critic_loss /= total_batches
actor_loss /= total_batches
actor_clip_frac /= total_batches
critic_loss = MPIUtil.reduce_avg(critic_loss)
actor_loss = MPIUtil.reduce_avg(actor_loss)
actor_clip_frac = MPIUtil.reduce_avg(actor_clip_frac)
critic_stepsize = self.critic_solver.get_stepsize()
actor_stepsize = self.update_actor_stepsize(actor_clip_frac)
self.logger.log_tabular('Critic_Loss', critic_loss)
self.logger.log_tabular('Critic_Stepsize', critic_stepsize)
self.logger.log_tabular('Actor_Loss', actor_loss)
self.logger.log_tabular('Actor_Stepsize', actor_stepsize)
self.logger.log_tabular('Clip_Frac', actor_clip_frac)
self.logger.log_tabular('Adv_Mean', adv_mean)
self.logger.log_tabular('Adv_Std', adv_std)
self.replay_buffer.clear()
return
def _get_iters_per_update(self):
return 1
def _valid_train_step(self):
samples = self.replay_buffer.get_current_size()
exp_samples = self.replay_buffer.count_filtered(self.EXP_ACTION_FLAG)
global_sample_count = int(MPIUtil.reduce_sum(samples))
global_exp_min = int(MPIUtil.reduce_min(exp_samples))
return (global_sample_count > self.batch_size) and (global_exp_min > 0)
def _compute_batch_vals(self, start_idx, end_idx):
states = self.replay_buffer.get_all("states")[start_idx:end_idx]
goals = self.replay_buffer.get_all(
"goals")[start_idx:end_idx] if self.has_goal() else None
idx = np.array(list(range(start_idx, end_idx)))
is_end = self.replay_buffer.is_path_end(idx)
is_fail = self.replay_buffer.check_terminal_flag(
idx, Env.Terminate.Fail)
is_succ = self.replay_buffer.check_terminal_flag(
idx, Env.Terminate.Succ)
is_fail = np.logical_and(is_end, is_fail)
is_succ = np.logical_and(is_end, is_succ)
vals = self._eval_critic(states, goals)
vals[is_fail] = self.val_fail
vals[is_succ] = self.val_succ
return vals
def _compute_batch_new_vals(self, start_idx, end_idx, val_buffer):
rewards = self.replay_buffer.get_all("rewards")[start_idx:end_idx]
if self.discount == 0:
new_vals = rewards.copy()
else:
new_vals = np.zeros_like(val_buffer)
curr_idx = start_idx
while curr_idx < end_idx:
idx0 = curr_idx - start_idx
idx1 = self.replay_buffer.get_path_end(curr_idx) - start_idx
r = rewards[idx0:idx1]
v = val_buffer[idx0:(idx1 + 1)]
new_vals[idx0:idx1] = RLUtil.compute_return(
r, self.discount, self.td_lambda, v)
curr_idx = idx1 + start_idx + 1
return new_vals
def _update_critic(self, s, g, tar_vals):
feed = {self.s_tf: s, self.g_tf: g, self.tar_val_tf: tar_vals}
loss, grads = self.sess.run([self.critic_loss_tf, self.critic_grad_tf],
feed)
self.critic_solver.update(grads)
return loss
def _update_actor(self, s, g, a, logp, adv):
feed = {
self.s_tf: s,
self.g_tf: g,
self.a_tf: a,
self.adv_tf: adv,
self.old_logp_tf: logp
}
loss, grads, clip_frac = self.sess.run(
[self.actor_loss_tf, self.actor_grad_tf, self.clip_frac_tf], feed)
self.actor_solver.update(grads)
return loss, clip_frac
def update_actor_stepsize(self, clip_frac):
clip_tol = 1.5
step_scale = 2
max_stepsize = 1e-2
min_stepsize = 1e-8
warmup_iters = 5
actor_stepsize = self.actor_solver.get_stepsize()
if (self.tar_clip_frac >= 0 and self.iter > warmup_iters):
min_clip = self.tar_clip_frac / clip_tol
max_clip = self.tar_clip_frac * clip_tol
under_tol = clip_frac < min_clip
over_tol = clip_frac > max_clip
if (over_tol or under_tol):
if (over_tol):
actor_stepsize *= self.actor_stepsize_decay
else:
actor_stepsize /= self.actor_stepsize_decay
actor_stepsize = np.clip(actor_stepsize, min_stepsize,
max_stepsize)
self.set_actor_stepsize(actor_stepsize)
return actor_stepsize
def set_actor_stepsize(self, stepsize):
feed = {
self._actor_stepsize_ph: stepsize,
}
self.sess.run(self._actor_stepsize_update_op, feed)
return
class PPOAgent(PGAgent):
NAME = "PPO"
EPOCHS_KEY = "Epochs"
BATCH_SIZE_KEY = "BatchSize"
RATIO_CLIP_KEY = "RatioClip"
NORM_ADV_CLIP_KEY = "NormAdvClip"
TD_LAMBDA_KEY = "TDLambda"
TAR_CLIP_FRAC = "TarClipFrac"
ACTOR_STEPSIZE_DECAY = "ActorStepsizeDecay"
def __init__(self, world, id, json_data):
super().__init__(world, id, json_data)
return
def _load_params(self, json_data):
super()._load_params(json_data)
self.epochs = 1 if (self.EPOCHS_KEY not in json_data) else json_data[self.EPOCHS_KEY]
self.batch_size = 1024 if (
self.BATCH_SIZE_KEY not in json_data) else json_data[self.BATCH_SIZE_KEY]
self.ratio_clip = 0.2 if (
self.RATIO_CLIP_KEY not in json_data) else json_data[self.RATIO_CLIP_KEY]
self.norm_adv_clip = 5 if (
self.NORM_ADV_CLIP_KEY not in json_data) else json_data[self.NORM_ADV_CLIP_KEY]
self.td_lambda = 0.95 if (
self.TD_LAMBDA_KEY not in json_data) else json_data[self.TD_LAMBDA_KEY]
self.tar_clip_frac = -1 if (
self.TAR_CLIP_FRAC not in json_data) else json_data[self.TAR_CLIP_FRAC]
self.actor_stepsize_decay = 0.5 if (
self.ACTOR_STEPSIZE_DECAY not in json_data) else json_data[self.ACTOR_STEPSIZE_DECAY]
num_procs = MPIUtil.get_num_procs()
local_batch_size = int(self.batch_size / num_procs)
min_replay_size = 2 * local_batch_size # needed to prevent buffer overflow
assert (self.replay_buffer_size > min_replay_size)
self.replay_buffer_size = np.maximum(min_replay_size, self.replay_buffer_size)
return
def _build_nets(self, json_data):
assert self.ACTOR_NET_KEY in json_data
assert self.CRITIC_NET_KEY in json_data
actor_net_name = json_data[self.ACTOR_NET_KEY]
critic_net_name = json_data[self.CRITIC_NET_KEY]
actor_init_output_scale = 1 if (self.ACTOR_INIT_OUTPUT_SCALE_KEY not in json_data
) else json_data[self.ACTOR_INIT_OUTPUT_SCALE_KEY]
s_size = self.get_state_size()
g_size = self.get_goal_size()
a_size = self.get_action_size()
# setup input tensors
self.s_tf = tf.placeholder(tf.float32, shape=[None, s_size], name="s")
self.a_tf = tf.placeholder(tf.float32, shape=[None, a_size], name="a")
self.tar_val_tf = tf.placeholder(tf.float32, shape=[None], name="tar_val")
self.adv_tf = tf.placeholder(tf.float32, shape=[None], name="adv")
self.g_tf = tf.placeholder(tf.float32,
shape=([None, g_size] if self.has_goal() else None),
name="g")
self.old_logp_tf = tf.placeholder(tf.float32, shape=[None], name="old_logp")
self.exp_mask_tf = tf.placeholder(tf.float32, shape=[None], name="exp_mask")
with tf.variable_scope('main'):
with tf.variable_scope('actor'):
self.a_mean_tf = self._build_net_actor(actor_net_name, actor_init_output_scale)
with tf.variable_scope('critic'):
self.critic_tf = self._build_net_critic(critic_net_name)
if (self.a_mean_tf != None):
Logger.print2('Built actor net: ' + actor_net_name)
if (self.critic_tf != None):
Logger.print2('Built critic net: ' + critic_net_name)
self.norm_a_std_tf = self.exp_params_curr.noise * tf.ones(a_size)
norm_a_noise_tf = self.norm_a_std_tf * tf.random_normal(shape=tf.shape(self.a_mean_tf))
norm_a_noise_tf *= tf.expand_dims(self.exp_mask_tf, axis=-1)
self.sample_a_tf = self.a_mean_tf + norm_a_noise_tf * self.a_norm.std_tf
self.sample_a_logp_tf = TFUtil.calc_logp_gaussian(x_tf=norm_a_noise_tf,
mean_tf=None,
std_tf=self.norm_a_std_tf)
return
def _build_losses(self, json_data):
actor_weight_decay = 0 if (
self.ACTOR_WEIGHT_DECAY_KEY not in json_data) else json_data[self.ACTOR_WEIGHT_DECAY_KEY]
critic_weight_decay = 0 if (
self.CRITIC_WEIGHT_DECAY_KEY not in json_data) else json_data[self.CRITIC_WEIGHT_DECAY_KEY]
norm_val_diff = self.val_norm.normalize_tf(self.tar_val_tf) - self.val_norm.normalize_tf(
self.critic_tf)
self.critic_loss_tf = 0.5 * tf.reduce_mean(tf.square(norm_val_diff))
if (critic_weight_decay != 0):
self.critic_loss_tf += critic_weight_decay * self._weight_decay_loss('main/critic')
norm_tar_a_tf = self.a_norm.normalize_tf(self.a_tf)
self._norm_a_mean_tf = self.a_norm.normalize_tf(self.a_mean_tf)
self.logp_tf = TFUtil.calc_logp_gaussian(norm_tar_a_tf, self._norm_a_mean_tf,
self.norm_a_std_tf)
ratio_tf = tf.exp(self.logp_tf - self.old_logp_tf)
actor_loss0 = self.adv_tf * ratio_tf
actor_loss1 = self.adv_tf * tf.clip_by_value(ratio_tf, 1.0 - self.ratio_clip,
1 + self.ratio_clip)
self.actor_loss_tf = -tf.reduce_mean(tf.minimum(actor_loss0, actor_loss1))
norm_a_bound_min = self.a_norm.normalize(self.a_bound_min)
norm_a_bound_max = self.a_norm.normalize(self.a_bound_max)
a_bound_loss = TFUtil.calc_bound_loss(self._norm_a_mean_tf, norm_a_bound_min, norm_a_bound_max)
self.actor_loss_tf += a_bound_loss
if (actor_weight_decay != 0):
self.actor_loss_tf += actor_weight_decay * self._weight_decay_loss('main/actor')
# for debugging
self.clip_frac_tf = tf.reduce_mean(
tf.to_float(tf.greater(tf.abs(ratio_tf - 1.0), self.ratio_clip)))
return
def _build_solvers(self, json_data):
actor_stepsize = 0.001 if (
self.ACTOR_STEPSIZE_KEY not in json_data) else json_data[self.ACTOR_STEPSIZE_KEY]
actor_momentum = 0.9 if (
self.ACTOR_MOMENTUM_KEY not in json_data) else json_data[self.ACTOR_MOMENTUM_KEY]
critic_stepsize = 0.01 if (
self.CRITIC_STEPSIZE_KEY not in json_data) else json_data[self.CRITIC_STEPSIZE_KEY]
critic_momentum = 0.9 if (
self.CRITIC_MOMENTUM_KEY not in json_data) else json_data[self.CRITIC_MOMENTUM_KEY]
critic_vars = self._tf_vars('main/critic')
critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize,
momentum=critic_momentum)
self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars)
self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars)
self._actor_stepsize_tf = tf.get_variable(dtype=tf.float32,
name='actor_stepsize',
initializer=actor_stepsize,
trainable=False)
self._actor_stepsize_ph = tf.get_variable(dtype=tf.float32, name='actor_stepsize_ph', shape=[])
self._actor_stepsize_update_op = self._actor_stepsize_tf.assign(self._actor_stepsize_ph)
actor_vars = self._tf_vars('main/actor')
actor_opt = tf.train.MomentumOptimizer(learning_rate=self._actor_stepsize_tf,
momentum=actor_momentum)
self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars)
self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars)
return
def _decide_action(self, s, g):
with self.sess.as_default(), self.graph.as_default():
self._exp_action = self._enable_stoch_policy() and MathUtil.flip_coin(
self.exp_params_curr.rate)
#print("_decide_action._exp_action=",self._exp_action)
a, logp = self._eval_actor(s, g, self._exp_action)
return a[0], logp[0]
def _eval_actor(self, s, g, enable_exp):
s = np.reshape(s, [-1, self.get_state_size()])
g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None
feed = {self.s_tf: s, self.g_tf: g, self.exp_mask_tf: np.array([1 if enable_exp else 0])}
a, logp = self.sess.run([self.sample_a_tf, self.sample_a_logp_tf], feed_dict=feed)
return a, logp
def _train_step(self):
adv_eps = 1e-5
start_idx = self.replay_buffer.buffer_tail
end_idx = self.replay_buffer.buffer_head
assert (start_idx == 0)
assert (self.replay_buffer.get_current_size() <= self.replay_buffer.buffer_size
) # must avoid overflow
assert (start_idx < end_idx)
idx = np.array(list(range(start_idx, end_idx)))
end_mask = self.replay_buffer.is_path_end(idx)
end_mask = np.logical_not(end_mask)
vals = self._compute_batch_vals(start_idx, end_idx)
new_vals = self._compute_batch_new_vals(start_idx, end_idx, vals)
valid_idx = idx[end_mask]
exp_idx = self.replay_buffer.get_idx_filtered(self.EXP_ACTION_FLAG).copy()
num_valid_idx = valid_idx.shape[0]
num_exp_idx = exp_idx.shape[0]
exp_idx = np.column_stack([exp_idx, np.array(list(range(0, num_exp_idx)), dtype=np.int32)])
local_sample_count = valid_idx.size
global_sample_count = int(MPIUtil.reduce_sum(local_sample_count))
mini_batches = int(np.ceil(global_sample_count / self.mini_batch_size))
adv = new_vals[exp_idx[:, 0]] - vals[exp_idx[:, 0]]
new_vals = np.clip(new_vals, self.val_min, self.val_max)
adv_mean = np.mean(adv)
adv_std = np.std(adv)
adv = (adv - adv_mean) / (adv_std + adv_eps)
adv = np.clip(adv, -self.norm_adv_clip, self.norm_adv_clip)
critic_loss = 0
actor_loss = 0
actor_clip_frac = 0
for e in range(self.epochs):
np.random.shuffle(valid_idx)
np.random.shuffle(exp_idx)
for b in range(mini_batches):
batch_idx_beg = b * self._local_mini_batch_size
batch_idx_end = batch_idx_beg + self._local_mini_batch_size
critic_batch = np.array(range(batch_idx_beg, batch_idx_end), dtype=np.int32)
actor_batch = critic_batch.copy()
critic_batch = np.mod(critic_batch, num_valid_idx)
actor_batch = np.mod(actor_batch, num_exp_idx)
shuffle_actor = (actor_batch[-1] < actor_batch[0]) or (actor_batch[-1] == num_exp_idx - 1)
critic_batch = valid_idx[critic_batch]
actor_batch = exp_idx[actor_batch]
critic_batch_vals = new_vals[critic_batch]
actor_batch_adv = adv[actor_batch[:, 1]]
critic_s = self.replay_buffer.get('states', critic_batch)
critic_g = self.replay_buffer.get('goals', critic_batch) if self.has_goal() else None
curr_critic_loss = self._update_critic(critic_s, critic_g, critic_batch_vals)
actor_s = self.replay_buffer.get("states", actor_batch[:, 0])
actor_g = self.replay_buffer.get("goals", actor_batch[:, 0]) if self.has_goal() else None
actor_a = self.replay_buffer.get("actions", actor_batch[:, 0])
actor_logp = self.replay_buffer.get("logps", actor_batch[:, 0])
curr_actor_loss, curr_actor_clip_frac = self._update_actor(actor_s, actor_g, actor_a,
actor_logp, actor_batch_adv)
critic_loss += curr_critic_loss
actor_loss += np.abs(curr_actor_loss)
actor_clip_frac += curr_actor_clip_frac
if (shuffle_actor):
np.random.shuffle(exp_idx)
total_batches = mini_batches * self.epochs
critic_loss /= total_batches
actor_loss /= total_batches
actor_clip_frac /= total_batches
critic_loss = MPIUtil.reduce_avg(critic_loss)
actor_loss = MPIUtil.reduce_avg(actor_loss)
actor_clip_frac = MPIUtil.reduce_avg(actor_clip_frac)
critic_stepsize = self.critic_solver.get_stepsize()
actor_stepsize = self.update_actor_stepsize(actor_clip_frac)
self.logger.log_tabular('Critic_Loss', critic_loss)
self.logger.log_tabular('Critic_Stepsize', critic_stepsize)
self.logger.log_tabular('Actor_Loss', actor_loss)
self.logger.log_tabular('Actor_Stepsize', actor_stepsize)
self.logger.log_tabular('Clip_Frac', actor_clip_frac)
self.logger.log_tabular('Adv_Mean', adv_mean)
self.logger.log_tabular('Adv_Std', adv_std)
self.replay_buffer.clear()
return
def _get_iters_per_update(self):
return 1
def _valid_train_step(self):
samples = self.replay_buffer.get_current_size()
exp_samples = self.replay_buffer.count_filtered(self.EXP_ACTION_FLAG)
global_sample_count = int(MPIUtil.reduce_sum(samples))
global_exp_min = int(MPIUtil.reduce_min(exp_samples))
return (global_sample_count > self.batch_size) and (global_exp_min > 0)
def _compute_batch_vals(self, start_idx, end_idx):
states = self.replay_buffer.get_all("states")[start_idx:end_idx]
goals = self.replay_buffer.get_all("goals")[start_idx:end_idx] if self.has_goal() else None
idx = np.array(list(range(start_idx, end_idx)))
is_end = self.replay_buffer.is_path_end(idx)
is_fail = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Fail)
is_succ = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Succ)
is_fail = np.logical_and(is_end, is_fail)
is_succ = np.logical_and(is_end, is_succ)
vals = self._eval_critic(states, goals)
vals[is_fail] = self.val_fail
vals[is_succ] = self.val_succ
return vals
def _compute_batch_new_vals(self, start_idx, end_idx, val_buffer):
rewards = self.replay_buffer.get_all("rewards")[start_idx:end_idx]
if self.discount == 0:
new_vals = rewards.copy()
else:
new_vals = np.zeros_like(val_buffer)
curr_idx = start_idx
while curr_idx < end_idx:
idx0 = curr_idx - start_idx
idx1 = self.replay_buffer.get_path_end(curr_idx) - start_idx
r = rewards[idx0:idx1]
v = val_buffer[idx0:(idx1 + 1)]
new_vals[idx0:idx1] = RLUtil.compute_return(r, self.discount, self.td_lambda, v)
curr_idx = idx1 + start_idx + 1
return new_vals
def _update_critic(self, s, g, tar_vals):
feed = {self.s_tf: s, self.g_tf: g, self.tar_val_tf: tar_vals}
loss, grads = self.sess.run([self.critic_loss_tf, self.critic_grad_tf], feed)
self.critic_solver.update(grads)
return loss
def _update_actor(self, s, g, a, logp, adv):
feed = {self.s_tf: s, self.g_tf: g, self.a_tf: a, self.adv_tf: adv, self.old_logp_tf: logp}
loss, grads, clip_frac = self.sess.run(
[self.actor_loss_tf, self.actor_grad_tf, self.clip_frac_tf], feed)
self.actor_solver.update(grads)
return loss, clip_frac
def update_actor_stepsize(self, clip_frac):
clip_tol = 1.5
step_scale = 2
max_stepsize = 1e-2
min_stepsize = 1e-8
warmup_iters = 5
actor_stepsize = self.actor_solver.get_stepsize()
if (self.tar_clip_frac >= 0 and self.iter > warmup_iters):
min_clip = self.tar_clip_frac / clip_tol
max_clip = self.tar_clip_frac * clip_tol
under_tol = clip_frac < min_clip
over_tol = clip_frac > max_clip
if (over_tol or under_tol):
if (over_tol):
actor_stepsize *= self.actor_stepsize_decay
else:
actor_stepsize /= self.actor_stepsize_decay
actor_stepsize = np.clip(actor_stepsize, min_stepsize, max_stepsize)
self.set_actor_stepsize(actor_stepsize)
return actor_stepsize
def set_actor_stepsize(self, stepsize):
feed = {
self._actor_stepsize_ph: stepsize,
}
self.sess.run(self._actor_stepsize_update_op, feed)
return