def train(self, memories):
s, visual_s, a, dc_r, cell_state = memories
with tf.device(self.device):
with tf.GradientTape() as tape:
output, cell_state = self.net(s,
visual_s,
cell_state=cell_state)
if self.is_continuous:
mu, log_std = output
log_act_prob = gaussian_likelihood_sum(a, mu, log_std)
entropy = gaussian_entropy(log_std)
else:
logits = output
logp_all = tf.nn.log_softmax(logits)
log_act_prob = tf.reduce_sum(tf.multiply(logp_all, a),
axis=1,
keepdims=True)
entropy = -tf.reduce_mean(
tf.reduce_sum(tf.exp(logp_all) * logp_all,
axis=1,
keepdims=True))
loss = -tf.reduce_mean(log_act_prob * dc_r)
loss_grads = tape.gradient(loss, self.net.trainable_variables)
self.optimizer.apply_gradients(
zip(loss_grads, self.net.trainable_variables))
self.global_step.assign_add(1)
return loss, entropy
def _get_action(self, s, visual_s, cell_state, options):
with tf.device(self.device):
(q, pi, beta), cell_state = self.net(
s, visual_s,
cell_state=cell_state) # [B, P], [B, P, A], [B, P], [B, P]
options_onehot = tf.one_hot(options,
self.options_num,
dtype=tf.float32) # [B, P]
options_onehot_expanded = tf.expand_dims(options_onehot,
axis=-1) # [B, P, 1]
pi = tf.reduce_sum(pi * options_onehot_expanded, axis=1) # [B, A]
if self.is_continuous:
mu = pi
log_std = tf.gather(self.log_std, options)
sample_op, _ = gaussian_clip_rsample(mu, log_std)
log_prob = gaussian_likelihood_sum(sample_op, mu, log_std)
else:
logits = pi
norm_dist = tfp.distributions.Categorical(
logits=tf.nn.log_softmax(logits))
sample_op = norm_dist.sample()
log_prob = norm_dist.log_prob(sample_op)
q_o = tf.reduce_sum(q * options_onehot, axis=-1) # [B, ]
beta_adv = q_o - ((1 - self.eps) * tf.reduce_max(q, axis=-1) +
self.eps * tf.reduce_mean(q, axis=-1)) # [B, ]
max_options = tf.cast(tf.argmax(q, axis=-1),
dtype=tf.int32) # [B, P] => [B, ]
beta_probs = tf.reduce_sum(beta * options_onehot,
axis=1) # [B, P] => [B,]
beta_dist = tfp.distributions.Bernoulli(probs=beta_probs)
new_options = tf.where(beta_dist.sample() < 1, options,
max_options) # <1 则不改变op， =1 则改变op
return sample_op, q_o, log_prob, beta_adv, new_options, max_options, cell_state
def train_actor(self, memories):
s, visual_s, a, old_log_prob, advantage, cell_state = memories
with tf.device(self.device):
with tf.GradientTape() as tape:
output, _ = self.net(s, visual_s, cell_state=cell_state)
if self.is_continuous:
mu, log_std = output
new_log_prob = gaussian_likelihood_sum(a, mu, log_std)
entropy = gaussian_entropy(log_std)
else:
logits = output
logp_all = tf.nn.log_softmax(logits)
new_log_prob = tf.reduce_sum(a * logp_all,
axis=1,
keepdims=True)
entropy = -tf.reduce_mean(
tf.reduce_sum(tf.exp(logp_all) * logp_all,
axis=1,
keepdims=True))
ratio = tf.exp(new_log_prob - old_log_prob)
actor_loss = -tf.reduce_mean(ratio * advantage)
actor_grads = tape.gradient(actor_loss,
self.net.actor_trainable_variables)
gradients = flat_concat(actor_grads)
self.global_step.assign_add(1)
return actor_loss, entropy, gradients
def train(self, memories):
s, visual_s, a, dc_r, cell_state = memories
with tf.device(self.device):
with tf.GradientTape(persistent=True) as tape:
feat, _ = self._representation_net(s, visual_s, cell_state=cell_state)
if self.is_continuous:
mu, log_std = self.net.policy_net(feat)
log_act_prob = gaussian_likelihood_sum(a, mu, log_std)
entropy = gaussian_entropy(log_std)
else:
logits = self.net.policy_net(feat)
logp_all = tf.nn.log_softmax(logits)
log_act_prob = tf.reduce_sum(a * logp_all, axis=1, keepdims=True)
entropy = -tf.reduce_mean(tf.reduce_sum(tf.exp(logp_all) * logp_all, axis=1, keepdims=True))
v = self.net.value_net(feat)
advantage = tf.stop_gradient(dc_r - v)
td_error = dc_r - v
critic_loss = tf.reduce_mean(tf.square(td_error))
actor_loss = -(tf.reduce_mean(log_act_prob * advantage) + self.beta * entropy)
critic_grads = tape.gradient(critic_loss, self.net.critic_trainable_variables)
self.optimizer_critic.apply_gradients(
zip(critic_grads, self.net.critic_trainable_variables)
)
if self.is_continuous:
actor_grads = tape.gradient(actor_loss, self.net.actor_trainable_variables)
self.optimizer_actor.apply_gradients(
zip(actor_grads, self.net.actor_trainable_variables)
)
else:
actor_grads = tape.gradient(actor_loss, self.net.actor_trainable_variables)
self.optimizer_actor.apply_gradients(
zip(actor_grads, self.net.actor_trainable_variables)
)
self.global_step.assign_add(1)
return actor_loss, critic_loss, entropy
def _train(self, memories, isw, cell_state):
ss, vvss, a, r, done, old_log_prob = memories
with tf.device(self.device):
with tf.GradientTape(persistent=True) as tape:
(feat,
feat_), _ = self._representation_net(ss,
vvss,
cell_state=cell_state,
need_split=True)
if self.is_continuous:
mu, log_std = self.net.policy_net(feat)
log_prob = gaussian_likelihood_sum(a, mu, log_std)
entropy = gaussian_entropy(log_std)
next_mu, _ = self.net.policy_net(feat_)
max_q_next = tf.stop_gradient(
self.net.value_net(feat_, next_mu))
else:
logits = self.net.policy_net(feat)
logp_all = tf.nn.log_softmax(logits)
log_prob = tf.reduce_sum(tf.multiply(logp_all, a),
axis=1,
keepdims=True)
entropy = -tf.reduce_mean(
tf.reduce_sum(tf.exp(logp_all) * logp_all,
axis=1,
keepdims=True))
logits = self.net.policy_net(feat_)
max_a = tf.argmax(logits, axis=1)
max_a_one_hot = tf.one_hot(max_a, self.a_dim)
max_q_next = tf.stop_gradient(
self.net.value_net(feat_, max_a_one_hot))
q = self.net.value_net(feat, a)
ratio = tf.stop_gradient(tf.exp(log_prob - old_log_prob))
q_value = tf.stop_gradient(q)
td_error = q - (r + self.gamma * (1 - done) * max_q_next)
critic_loss = tf.reduce_mean(tf.square(td_error) * isw)
actor_loss = -tf.reduce_mean(ratio * log_prob * q_value)
critic_grads = tape.gradient(critic_loss,
self.net.critic_trainable_variables)
self.optimizer_critic.apply_gradients(
zip(critic_grads, self.net.critic_trainable_variables))
actor_grads = tape.gradient(actor_loss,
self.net.actor_trainable_variables)
self.optimizer_actor.apply_gradients(
zip(actor_grads, self.net.actor_trainable_variables))
self.global_step.assign_add(1)
return td_error, dict([['LOSS/actor_loss', actor_loss],
['LOSS/critic_loss', critic_loss],
['Statistics/q_max',
tf.reduce_max(q)],
['Statistics/q_min',
tf.reduce_min(q)],
['Statistics/q_mean',
tf.reduce_mean(q)],
['Statistics/ratio',
tf.reduce_mean(ratio)],
['Statistics/entropy', entropy]])
def train_share(self, memories, kl_coef, crsty_loss, cell_state):
s, visual_s, a, dc_r, old_log_prob, advantage, old_value = memories
with tf.device(self.device):
with tf.GradientTape() as tape:
feat = self.get_feature(s, visual_s, cell_state=cell_state)
if self.is_continuous:
mu, value = self.net(feat)
new_log_prob = gaussian_likelihood_sum(a, mu, self.log_std)
entropy = gaussian_entropy(self.log_std)
else:
logits, value = self.net(feat)
logp_all = tf.nn.log_softmax(logits)
new_log_prob = tf.reduce_sum(a * logp_all,
axis=1,
keepdims=True)
entropy = -tf.reduce_mean(
tf.reduce_sum(tf.exp(logp_all) * logp_all,
axis=1,
keepdims=True))
ratio = tf.exp(new_log_prob - old_log_prob)
# https://github.com/joschu/modular_rl/blob/6970cde3da265cf2a98537250fea5e0c0d9a7639/modular_rl/ppo.py#L40
if self.kl_reverse:
kl = tf.reduce_mean(new_log_prob - old_log_prob)
else:
kl = tf.reduce_mean(
old_log_prob - new_log_prob
) # a sample estimate for KL-divergence, easy to compute
surrogate = ratio * advantage
# https://github.com/llSourcell/OpenAI_Five_vs_Dota2_Explained/blob/c5def7e57aa70785c2394ea2eeb3e5f66ad59a53/train.py#L154
value_clip = old_value + tf.clip_by_value(
value - old_value, -self.value_epsilon, self.value_epsilon)
td_error = dc_r - value
td_error_clip = dc_r - value_clip
td_square = tf.maximum(tf.square(td_error),
tf.square(td_error_clip))
pi_loss = -tf.reduce_mean(
tf.minimum(
surrogate,
tf.clip_by_value(ratio, 1.0 - self.epsilon,
1.0 + self.epsilon) * advantage))
kl_loss = kl_coef * kl
extra_loss = 1000.0 * tf.square(
tf.maximum(0., kl - self.kl_cutoff))
actor_loss = pi_loss + kl_loss + extra_loss
value_loss = 0.5 * tf.reduce_mean(td_square)
loss = actor_loss + 1.0 * value_loss - self.beta * entropy + crsty_loss
loss_grads = tape.gradient(loss, self.net_tv)
self.optimizer.apply_gradients(zip(loss_grads, self.net_tv))
self.global_step.assign_add(1)
return actor_loss, value_loss, entropy, kl
def _get_action(self, obs, cell_state):
with tf.device(self.device):
output, cell_state = self.net(obs, cell_state=cell_state)
if self.is_continuous:
mu, log_std = output
sample_op, _ = gaussian_clip_rsample(mu, log_std)
log_prob = gaussian_likelihood_sum(sample_op, mu, log_std)
else:
logits = output
norm_dist = tfp.distributions.Categorical(logits=tf.nn.log_softmax(logits))
sample_op = norm_dist.sample()
log_prob = norm_dist.log_prob(sample_op)
return sample_op, log_prob, cell_state
def train_actor(self, BATCH, cell_state, kl_coef):
with tf.device(self.device):
with tf.GradientTape() as tape:
output, _ = self.net(BATCH.obs, cell_state=cell_state)
if self.is_continuous:
mu, log_std = output
new_log_prob = gaussian_likelihood_sum(
BATCH.action, mu, log_std)
entropy = gaussian_entropy(log_std)
else:
logits = output
logp_all = tf.nn.log_softmax(logits)
new_log_prob = tf.reduce_sum(BATCH.action * logp_all,
axis=1,
keepdims=True)
entropy = -tf.reduce_mean(
tf.reduce_sum(tf.exp(logp_all) * logp_all,
axis=1,
keepdims=True))
ratio = tf.exp(new_log_prob - BATCH.log_prob)
kl = tf.reduce_mean(BATCH.log_prob - new_log_prob)
surrogate = ratio * BATCH.gae_adv
clipped_surrogate = tf.minimum(
surrogate,
tf.where(BATCH.gae_adv > 0,
(1 + self.epsilon) * BATCH.gae_adv,
(1 - self.epsilon) * BATCH.gae_adv))
if self.use_duel_clip:
clipped_surrogate = tf.maximum(clipped_surrogate,
(1.0 + self.duel_epsilon) *
BATCH.gae_adv)
actor_loss = -(tf.reduce_mean(clipped_surrogate) +
self.ent_coef * entropy)
if self.use_kl_loss:
kl_loss = kl_coef * kl
actor_loss += kl_loss
if self.use_extra_loss:
extra_loss = self.extra_coef * tf.square(
tf.maximum(0., kl - self.kl_cutoff))
actor_loss += extra_loss
actor_grads = tape.gradient(actor_loss,
self.net.actor_trainable_variables)
self.optimizer_actor.apply_gradients(
zip(actor_grads, self.net.actor_trainable_variables))
self.global_step.assign_add(1)
return actor_loss, entropy, kl
def _get_action(self, s, visual_s, cell_state):
with tf.device(self.device):
feat, cell_state = self.get_feature(s, visual_s, cell_state=cell_state, record_cs=True)
value = self.critic_net(feat)
if self.is_continuous:
mu = self.actor_net(feat)
sample_op, _ = gaussian_clip_rsample(mu, self.log_std)
log_prob = gaussian_likelihood_sum(sample_op, mu, self.log_std)
return sample_op, value, log_prob, mu, cell_state
else:
logits = self.actor_net(feat)
logp_all = tf.nn.log_softmax(logits)
norm_dist = tfp.distributions.Categorical(logits)
sample_op = norm_dist.sample()
log_prob = norm_dist.log_prob(sample_op)
return sample_op, value, log_prob, logp_all, cell_state
def _get_action(self, obs, cell_state):
with tf.device(self.device):
feat, cell_state = self._representation_net(obs,
cell_state=cell_state)
value = self.net.value_net(feat)
output = self.net.policy_net(feat)
if self.is_continuous:
mu, log_std = output
sample_op, _ = gaussian_clip_rsample(mu, log_std)
log_prob = gaussian_likelihood_sum(sample_op, mu, log_std)
return sample_op, value, log_prob, (mu, log_std), cell_state
else:
logits = output
logp_all = tf.nn.log_softmax(logits)
norm_dist = tfp.distributions.Categorical(logits=logp_all)
sample_op = norm_dist.sample()
log_prob = norm_dist.log_prob(sample_op)
return sample_op, value, log_prob, logp_all, cell_state
def train_actor(self, memories, kl_coef, cell_state):
s, visual_s, a, old_log_prob, advantage = memories
with tf.device(self.device):
feat = self.get_feature(s, visual_s, cell_state=cell_state)
with tf.GradientTape() as tape:
if self.is_continuous:
mu = self.actor_net(feat)
new_log_prob = gaussian_likelihood_sum(a, mu, self.log_std)
entropy = gaussian_entropy(self.log_std)
else:
logits = self.actor_net(feat)
logp_all = tf.nn.log_softmax(logits)
new_log_prob = tf.reduce_sum(a * logp_all,
axis=1,
keepdims=True)
entropy = -tf.reduce_mean(
tf.reduce_sum(tf.exp(logp_all) * logp_all,
axis=1,
keepdims=True))
ratio = tf.exp(new_log_prob - old_log_prob)
kl = tf.reduce_mean(old_log_prob - new_log_prob)
surrogate = ratio * advantage
min_adv = tf.where(advantage > 0,
(1 + self.epsilon) * advantage,
(1 - self.epsilon) * advantage)
pi_loss = -(tf.reduce_mean(tf.minimum(surrogate, min_adv)) +
self.beta * entropy)
kl_loss = kl_coef * kl
extra_loss = 1000.0 * tf.square(
tf.maximum(0., kl - self.kl_cutoff))
actor_loss = pi_loss + kl_loss + extra_loss
actor_grads = tape.gradient(actor_loss, self.actor_net_tv)
self.optimizer_actor.apply_gradients(
zip(actor_grads, self.actor_net_tv))
self.global_step.assign_add(1)
return actor_loss, entropy, kl
def _get_action(self, obs, cell_state):
with tf.device(self.device):
feat, cell_state = self._representation_net(obs,
cell_state=cell_state)
if self.is_continuous:
if self.share_net:
mu, log_std, value = self.net.value_net(feat)
else:
mu, log_std = self.net.policy_net(feat)
value = self.net.value_net(feat)
sample_op, _ = gaussian_clip_rsample(mu, log_std)
log_prob = gaussian_likelihood_sum(sample_op, mu, log_std)
else:
if self.share_net:
logits, value = self.net.value_net(feat)
else:
logits = self.net.policy_net(feat)
value = self.net.value_net(feat)
norm_dist = tfp.distributions.Categorical(
logits=tf.nn.log_softmax(logits))
sample_op = norm_dist.sample()
log_prob = norm_dist.log_prob(sample_op)
return sample_op, value, log_prob, cell_state
def train(self, memories, crsty_loss, cell_state):
s, visual_s, a, dc_r = memories
with tf.device(self.device):
with tf.GradientTape() as tape:
feat = self.get_feature(s, visual_s, cell_state=cell_state)
v = self.critic_net(feat)
td_error = dc_r - v
critic_loss = tf.reduce_mean(tf.square(td_error)) + crsty_loss
critic_grads = tape.gradient(critic_loss, self.critic_tv)
self.optimizer_critic.apply_gradients(
zip(critic_grads, self.critic_tv)
)
with tf.GradientTape() as tape:
if self.is_continuous:
mu = self.actor_net(feat)
log_act_prob = gaussian_likelihood_sum(a, mu, self.log_std)
entropy = gaussian_entropy(self.log_std)
else:
logits = self.actor_net(feat)
logp_all = tf.nn.log_softmax(logits)
log_act_prob = tf.reduce_sum(a * logp_all, axis=1, keepdims=True)
entropy = -tf.reduce_mean(tf.reduce_sum(tf.exp(logp_all) * logp_all, axis=1, keepdims=True))
v = self.critic_net(feat)
advantage = tf.stop_gradient(dc_r - v)
actor_loss = -(tf.reduce_mean(log_act_prob * advantage) + self.beta * entropy)
if self.is_continuous:
actor_grads = tape.gradient(actor_loss, self.actor_tv)
self.optimizer_actor.apply_gradients(
zip(actor_grads, self.actor_tv)
)
else:
actor_grads = tape.gradient(actor_loss, self.actor_tv)
self.optimizer_actor.apply_gradients(
zip(actor_grads, self.actor_tv)
)
self.global_step.assign_add(1)
return actor_loss, critic_loss, entropy
def _get_action(self, s, visual_s, cell_state, options):
with tf.device(self.device):
feat, cell_state = self.get_feature(s,
visual_s,
cell_state=cell_state,
record_cs=True)
q, pi, beta, o = self.net(
feat) # [B, P], [B, P, A], [B, P], [B, P]
options_onehot = tf.one_hot(options,
self.options_num,
dtype=tf.float32) # [B, P]
options_onehot_expanded = tf.expand_dims(options_onehot,
axis=-1) # [B, P, 1]
pi = tf.reduce_sum(pi * options_onehot_expanded, axis=1) # [B, A]
if self.is_continuous:
log_std = tf.gather(self.log_std, options)
mu = pi
sample_op, _ = gaussian_clip_rsample(mu, log_std)
log_prob = gaussian_likelihood_sum(sample_op, mu, log_std)
else:
logits = pi
norm_dist = tfp.distributions.Categorical(logits)
sample_op = norm_dist.sample()
log_prob = norm_dist.log_prob(sample_op)
o_log_prob = tf.reduce_sum(o * options_onehot, axis=-1) # [B, ]
q_o = tf.reduce_sum(q * options_onehot, axis=-1) # [B, ]
beta_adv = q_o - tf.reduce_sum(q * tf.math.exp(o),
axis=-1) # [B, ]
option_norm_dist = tfp.distributions.Categorical(
probs=tf.math.exp(o))
sample_options = option_norm_dist.sample()
beta_probs = tf.reduce_sum(beta * options_onehot,
axis=1) # [B, P] => [B,]
beta_dist = tfp.distributions.Bernoulli(probs=beta_probs)
new_options = tf.where(beta_dist.sample() < 1, options,
sample_options) # <1 则不改变op， =1 则改变op
return sample_op, q_o, log_prob, o_log_prob, beta_adv, new_options, cell_state
def _train(self, memories, isw, cell_state):
s, visual_s, a, r, s_, visual_s_, done, last_options, options = memories
last_options = tf.cast(last_options, tf.int32)
options = tf.cast(options, tf.int32)
with tf.device(self.device):
with tf.GradientTape(persistent=True) as tape:
feat, _ = self._representation_net(s,
visual_s,
cell_state=cell_state)
feat_, _ = self._representation_target_net(
s_, visual_s_, cell_state=cell_state)
q = self.q_net.value_net(feat) # [B, P]
pi = self.intra_option_net.value_net(feat) # [B, P, A]
beta = self.termination_net.value_net(feat) # [B, P]
q_next = self.q_target_net.value_net(
feat_) # [B, P], [B, P, A], [B, P]
beta_next = self.termination_net.value_net(feat_) # [B, P]
interests = self.interest_net.value_net(feat) # [B, P]
options_onehot = tf.one_hot(options,
self.options_num,
dtype=tf.float32) # [B,] => [B, P]
q_s = qu_eval = tf.reduce_sum(q * options_onehot,
axis=-1,
keepdims=True) # [B, 1]
beta_s_ = tf.reduce_sum(beta_next * options_onehot,
axis=-1,
keepdims=True) # [B, 1]
q_s_ = tf.reduce_sum(q_next * options_onehot,
axis=-1,
keepdims=True) # [B, 1]
if self.double_q:
q_ = self.q_net.value_net(
feat) # [B, P], [B, P, A], [B, P]
max_a_idx = tf.one_hot(
tf.argmax(q_, axis=-1),
self.options_num,
dtype=tf.float32) # [B, P] => [B, ] => [B, P]
q_s_max = tf.reduce_sum(q_next * max_a_idx,
axis=-1,
keepdims=True) # [B, 1]
else:
q_s_max = tf.reduce_max(q_next, axis=-1,
keepdims=True) # [B, 1]
u_target = (1 - beta_s_) * q_s_ + beta_s_ * q_s_max # [B, 1]
qu_target = tf.stop_gradient(r + self.gamma *
(1 - done) * u_target)
td_error = qu_target - qu_eval # gradient : q
q_loss = tf.reduce_mean(tf.square(td_error) *
isw) # [B, 1] => 1
if self.use_baseline:
adv = tf.stop_gradient(qu_target - qu_eval)
else:
adv = tf.stop_gradient(qu_target)
options_onehot_expanded = tf.expand_dims(
options_onehot, axis=-1) # [B, P] => [B, P, 1]
pi = tf.reduce_sum(pi * options_onehot_expanded,
axis=1) # [B, P, A] => [B, A]
if self.is_continuous:
log_std = tf.gather(self.log_std, options)
mu = tf.math.tanh(pi)
log_p = gaussian_likelihood_sum(a, mu, log_std)
entropy = gaussian_entropy(log_std)
else:
pi = pi / self.boltzmann_temperature
log_pi = tf.nn.log_softmax(pi, axis=-1) # [B, A]
entropy = -tf.reduce_sum(tf.exp(log_pi) * log_pi,
axis=1,
keepdims=True) # [B, 1]
log_p = tf.reduce_sum(a * log_pi, axis=-1,
keepdims=True) # [B, 1]
pi_loss = tf.reduce_mean(
-(log_p * adv + self.ent_coff * entropy)
) # [B, 1] * [B, 1] => [B, 1] => 1
last_options_onehot = tf.one_hot(
last_options, self.options_num,
dtype=tf.float32) # [B,] => [B, P]
beta_s = tf.reduce_sum(beta * last_options_onehot,
axis=-1,
keepdims=True) # [B, 1]
pi_op = tf.nn.softmax(
interests *
tf.stop_gradient(q)) # [B, P] or tf.nn.softmax(q)
interest_loss = -tf.reduce_mean(beta_s * tf.reduce_sum(
pi_op * options_onehot, axis=-1, keepdims=True) *
q_s) # [B, 1] => 1
v_s = tf.reduce_sum(q * pi_op, axis=-1,
keepdims=True) # [B, P] * [B, P] => [B, 1]
beta_loss = beta_s * tf.stop_gradient(q_s - v_s) # [B, 1]
if self.terminal_mask:
beta_loss *= (1 - done)
beta_loss = tf.reduce_mean(beta_loss) # [B, 1] => 1
q_grads = tape.gradient(q_loss, self.q_net.trainable_variables)
intra_option_grads = tape.gradient(pi_loss, self.actor_tv)
termination_grads = tape.gradient(
beta_loss, self.termination_net.trainable_variables)
interest_grads = tape.gradient(
interest_loss, self.interest_net.trainable_variables)
self.q_optimizer.apply_gradients(
zip(q_grads, self.q_net.trainable_variables))
self.intra_option_optimizer.apply_gradients(
zip(intra_option_grads, self.actor_tv))
self.termination_optimizer.apply_gradients(
zip(termination_grads,
self.termination_net.trainable_variables))
self.interest_optimizer.apply_gradients(
zip(interest_grads, self.interest_net.trainable_variables))
self.global_step.assign_add(1)
return td_error, dict(
[['LOSS/q_loss', tf.reduce_mean(q_loss)],
['LOSS/pi_loss', tf.reduce_mean(pi_loss)],
['LOSS/beta_loss',
tf.reduce_mean(beta_loss)],
['LOSS/interest_loss',
tf.reduce_mean(interest_loss)],
['Statistics/q_option_max',
tf.reduce_max(q_s)],
['Statistics/q_option_min',
tf.reduce_min(q_s)],
['Statistics/q_option_mean',
tf.reduce_mean(q_s)]])
def train_share(self, BATCH, cell_state, kl_coef):
with tf.device(self.device):
with tf.GradientTape() as tape:
output, cell_state = self.net(BATCH.obs, cell_state=cell_state)
if self.is_continuous:
mu, log_std, value = output
new_log_prob = gaussian_likelihood_sum(
BATCH.action, mu, log_std)
entropy = gaussian_entropy(log_std)
else:
logits, value = output
logp_all = tf.nn.log_softmax(logits)
new_log_prob = tf.reduce_sum(BATCH.action * logp_all,
axis=1,
keepdims=True)
entropy = -tf.reduce_mean(
tf.reduce_sum(tf.exp(logp_all) * logp_all,
axis=1,
keepdims=True))
ratio = tf.exp(new_log_prob - BATCH.log_prob)
surrogate = ratio * BATCH.gae_adv
clipped_surrogate = tf.minimum(
surrogate,
tf.clip_by_value(ratio, 1.0 - self.epsilon,
1.0 + self.epsilon) * BATCH.gae_adv)
# ref: https://github.com/thu-ml/tianshou/blob/c97aa4065ee8464bd5897bb86f1f81abd8e2cff9/tianshou/policy/modelfree/ppo.py#L159
if self.use_duel_clip:
clipped_surrogate = tf.maximum(clipped_surrogate,
(1.0 + self.duel_epsilon) *
BATCH.gae_adv)
actor_loss = -(tf.reduce_mean(clipped_surrogate) +
self.ent_coef * entropy)
# ref: https://github.com/joschu/modular_rl/blob/6970cde3da265cf2a98537250fea5e0c0d9a7639/modular_rl/ppo.py#L40
# ref: https://github.com/hill-a/stable-baselines/blob/b3f414f4f2900403107357a2206f80868af16da3/stable_baselines/ppo2/ppo2.py#L185
if self.kl_reverse:
kl = .5 * tf.reduce_mean(
tf.square(new_log_prob - BATCH.log_prob))
else:
kl = .5 * tf.reduce_mean(
tf.square(BATCH.log_prob - new_log_prob)
) # a sample estimate for KL-divergence, easy to compute
td_error = BATCH.discounted_reward - value
if self.use_vclip:
# ref: https://github.com/llSourcell/OpenAI_Five_vs_Dota2_Explained/blob/c5def7e57aa70785c2394ea2eeb3e5f66ad59a53/train.py#L154
# ref: https://github.com/hill-a/stable-baselines/blob/b3f414f4f2900403107357a2206f80868af16da3/stable_baselines/ppo2/ppo2.py#L172
value_clip = BATCH.value + tf.clip_by_value(
value - BATCH.value, -self.value_epsilon,
self.value_epsilon)
td_error_clip = BATCH.discounted_reward - value_clip
td_square = tf.maximum(tf.square(td_error),
tf.square(td_error_clip))
else:
td_square = tf.square(td_error)
if self.use_kl_loss:
kl_loss = kl_coef * kl
actor_loss += kl_loss
if self.use_extra_loss:
extra_loss = self.extra_coef * tf.square(
tf.maximum(0., kl - self.kl_cutoff))
actor_loss += extra_loss
value_loss = 0.5 * tf.reduce_mean(td_square)
loss = actor_loss + self.vf_coef * value_loss
loss_grads = tape.gradient(loss, self.net.trainable_variables)
self.optimizer.apply_gradients(
zip(loss_grads, self.net.trainable_variables))
self.global_step.assign_add(1)
return actor_loss, value_loss, entropy, kl
def share(self, BATCH, cell_state, kl_coef):
last_options = tf.cast(BATCH.last_options, tf.int32) # [B,]
options = tf.cast(BATCH.options, tf.int32)
with tf.device(self.device):
with tf.GradientTape() as tape:
(q, pi, beta, o), cell_state = self.net(
BATCH.obs,
cell_state=cell_state) # [B, P], [B, P, A], [B, P], [B, P]
options_onehot = tf.one_hot(options,
self.options_num,
dtype=tf.float32) # [B, P]
options_onehot_expanded = tf.expand_dims(options_onehot,
axis=-1) # [B, P, 1]
last_options_onehot = tf.one_hot(
last_options, self.options_num,
dtype=tf.float32) # [B,] => [B, P]
pi = tf.reduce_sum(pi * options_onehot_expanded,
axis=1) # [B, P, A] => [B, A]
value = tf.reduce_sum(q * options_onehot,
axis=1,
keepdims=True) # [B, 1]
if self.is_continuous:
log_std = tf.gather(self.log_std, options)
mu = pi # [B, A]
new_log_prob = gaussian_likelihood_sum(
BATCH.action, mu, log_std)
entropy = gaussian_entropy(log_std)
else:
logits = pi # [B, A]
logp_all = tf.nn.log_softmax(logits)
new_log_prob = tf.reduce_sum(BATCH.action * logp_all,
axis=1,
keepdims=True)
entropy = -tf.reduce_mean(
tf.reduce_sum(tf.exp(logp_all) * logp_all,
axis=1,
keepdims=True))
ratio = tf.exp(new_log_prob - BATCH.log_prob)
if self.kl_reverse:
kl = tf.reduce_mean(new_log_prob - BATCH.log_prob)
else:
kl = tf.reduce_mean(
BATCH.log_prob - new_log_prob
) # a sample estimate for KL-divergence, easy to compute
surrogate = ratio * BATCH.gae_adv
value_clip = BATCH.value + tf.clip_by_value(
value - BATCH.value, -self.value_epsilon,
self.value_epsilon)
td_error = BATCH.discounted_reward - value
td_error_clip = BATCH.discounted_reward - value_clip
td_square = tf.maximum(tf.square(td_error),
tf.square(td_error_clip))
pi_loss = -tf.reduce_mean(
tf.minimum(
surrogate,
tf.clip_by_value(ratio, 1.0 - self.epsilon,
1.0 + self.epsilon) * BATCH.gae_adv))
kl_loss = kl_coef * kl
extra_loss = 1000.0 * tf.square(
tf.maximum(0., kl - self.kl_cutoff))
pi_loss = pi_loss + kl_loss + extra_loss
q_loss = 0.5 * tf.reduce_mean(td_square)
beta_s = tf.reduce_sum(beta * last_options_onehot,
axis=-1,
keepdims=True) # [B, 1]
beta_loss = tf.reduce_mean(beta_s * BATCH.beta_advantage)
if self.terminal_mask:
beta_loss *= (1 - done)
o_log_prob = tf.reduce_sum(o * options_onehot,
axis=-1,
keepdims=True) # [B, 1]
o_ratio = tf.exp(o_log_prob - BATCH.o_log_prob)
o_entropy = -tf.reduce_mean(
tf.reduce_sum(tf.exp(o) * o, axis=1, keepdims=True))
o_loss = -tf.reduce_mean(
tf.minimum(
o_ratio * BATCH.gae_adv,
tf.clip_by_value(o_ratio, 1.0 - self.epsilon,
1.0 + self.epsilon) * BATCH.gae_adv))
loss = pi_loss + 1.0 * q_loss + o_loss + beta_loss - self.pi_beta * entropy - self.o_beta * o_entropy
loss_grads = tape.gradient(loss, self.net_tv)
self.optimizer.apply_gradients(zip(loss_grads, self.net_tv))
self.global_step.assign_add(1)
return loss, pi_loss, q_loss, o_loss, beta_loss, entropy, o_entropy, kl
def _train(self, memories, isw, cell_state):
s, visual_s, a, r, s_, visual_s_, done, last_options, options = memories
last_options = tf.cast(last_options, tf.int32)
options = tf.cast(options, tf.int32)
with tf.device(self.device):
with tf.GradientTape(persistent=True) as tape:
feat, _ = self._representation_net(s,
visual_s,
cell_state=cell_state)
feat_, _ = self._representation_target_net(
s_, visual_s_, cell_state=cell_state)
q = self.q_net.value_net(feat) # [B, P]
pi = self.intra_option_net.value_net(feat) # [B, P, A]
beta = self.termination_net.value_net(feat) # [B, P]
q_next = self.q_target_net.value_net(
feat_) # [B, P], [B, P, A], [B, P]
beta_next = self.termination_net.value_net(feat_) # [B, P]
options_onehot = tf.one_hot(options,
self.options_num,
dtype=tf.float32) # [B,] => [B, P]
q_s = qu_eval = tf.reduce_sum(q * options_onehot,
axis=-1,
keepdims=True) # [B, 1]
beta_s_ = tf.reduce_sum(beta_next * options_onehot,
axis=-1,
keepdims=True) # [B, 1]
q_s_ = tf.reduce_sum(q_next * options_onehot,
axis=-1,
keepdims=True) # [B, 1]
# https://github.com/jeanharb/option_critic/blob/5d6c81a650a8f452bc8ad3250f1f211d317fde8c/neural_net.py#L94
if self.double_q:
q_ = self.q_net.value_net(
feat) # [B, P], [B, P, A], [B, P]
max_a_idx = tf.one_hot(
tf.argmax(q_, axis=-1),
self.options_num,
dtype=tf.float32) # [B, P] => [B, ] => [B, P]
q_s_max = tf.reduce_sum(q_next * max_a_idx,
axis=-1,
keepdims=True) # [B, 1]
else:
q_s_max = tf.reduce_max(q_next, axis=-1,
keepdims=True) # [B, 1]
u_target = (1 - beta_s_) * q_s_ + beta_s_ * q_s_max # [B, 1]
qu_target = tf.stop_gradient(r + self.gamma *
(1 - done) * u_target)
td_error = qu_target - qu_eval # gradient : q
q_loss = tf.reduce_mean(tf.square(td_error) *
isw) # [B, 1] => 1
# https://github.com/jeanharb/option_critic/blob/5d6c81a650a8f452bc8ad3250f1f211d317fde8c/neural_net.py#L130
if self.use_baseline:
adv = tf.stop_gradient(qu_target - qu_eval)
else:
adv = tf.stop_gradient(qu_target)
options_onehot_expanded = tf.expand_dims(
options_onehot, axis=-1) # [B, P] => [B, P, 1]
pi = tf.reduce_sum(pi * options_onehot_expanded,
axis=1) # [B, P, A] => [B, A]
if self.is_continuous:
log_std = tf.gather(self.log_std, options)
mu = tf.math.tanh(pi)
log_p = gaussian_likelihood_sum(a, mu, log_std)
entropy = gaussian_entropy(log_std)
else:
pi = pi / self.boltzmann_temperature
log_pi = tf.nn.log_softmax(pi, axis=-1) # [B, A]
entropy = -tf.reduce_sum(tf.exp(log_pi) * log_pi,
axis=1,
keepdims=True) # [B, 1]
log_p = tf.reduce_sum(a * log_pi, axis=-1,
keepdims=True) # [B, 1]
pi_loss = tf.reduce_mean(
-(log_p * adv + self.ent_coff * entropy)
) # [B, 1] * [B, 1] => [B, 1] => 1
last_options_onehot = tf.one_hot(
last_options, self.options_num,
dtype=tf.float32) # [B,] => [B, P]
beta_s = tf.reduce_sum(beta * last_options_onehot,
axis=-1,
keepdims=True) # [B, 1]
if self.use_eps_greedy:
v_s = tf.reduce_max(
q, axis=-1,
keepdims=True) - self.termination_regularizer # [B, 1]
else:
v_s = (1 - beta_s) * q_s + beta_s * tf.reduce_max(
q, axis=-1, keepdims=True) # [B, 1]
# v_s = tf.reduce_mean(q, axis=-1, keepdims=True) # [B, 1]
beta_loss = beta_s * tf.stop_gradient(q_s - v_s) # [B, 1]
# https://github.com/lweitkamp/option-critic-pytorch/blob/0c57da7686f8903ed2d8dded3fae832ee9defd1a/option_critic.py#L238
if self.terminal_mask:
beta_loss *= (1 - done)
beta_loss = tf.reduce_mean(beta_loss) # [B, 1] => 1
q_grads = tape.gradient(q_loss, self.q_net.trainable_variables)
intra_option_grads = tape.gradient(pi_loss, self.actor_tv)
termination_grads = tape.gradient(
beta_loss, self.termination_net.trainable_variables)
self.q_optimizer.apply_gradients(
zip(q_grads, self.q_net.trainable_variables))
self.intra_option_optimizer.apply_gradients(
zip(intra_option_grads, self.actor_tv))
self.termination_optimizer.apply_gradients(
zip(termination_grads,
self.termination_net.trainable_variables))
self.global_step.assign_add(1)
return td_error, dict(
[['LOSS/q_loss', tf.reduce_mean(q_loss)],
['LOSS/pi_loss', tf.reduce_mean(pi_loss)],
['LOSS/beta_loss',
tf.reduce_mean(beta_loss)],
['Statistics/q_option_max',
tf.reduce_max(q_s)],
['Statistics/q_option_min',
tf.reduce_min(q_s)],
['Statistics/q_option_mean',
tf.reduce_mean(q_s)]])
def train(self, memories, kl_coef):
s, visual_s, a, dc_r, old_log_prob, advantage, old_value, beta_advantage, last_options, options, cell_state = memories
last_options = tf.reshape(tf.cast(last_options, tf.int32),
(-1, )) # [B, 1] => [B,]
options = tf.reshape(tf.cast(options, tf.int32), (-1, ))
with tf.device(self.device):
with tf.GradientTape() as tape:
(q, pi, beta), cell_state = self.net(
s, visual_s,
cell_state=cell_state) # [B, P], [B, P, A], [B, P], [B, P]
options_onehot = tf.one_hot(options,
self.options_num,
dtype=tf.float32) # [B, P]
options_onehot_expanded = tf.expand_dims(options_onehot,
axis=-1) # [B, P, 1]
last_options_onehot = tf.one_hot(
last_options, self.options_num,
dtype=tf.float32) # [B,] => [B, P]
pi = tf.reduce_sum(pi * options_onehot_expanded,
axis=1) # [B, P, A] => [B, A]
value = tf.reduce_sum(q * options_onehot,
axis=1,
keepdims=True) # [B, 1]
if self.is_continuous:
mu = pi # [B, A]
log_std = tf.gather(self.log_std, options)
new_log_prob = gaussian_likelihood_sum(a, mu, log_std)
entropy = gaussian_entropy(log_std)
else:
logits = pi # [B, A]
logp_all = tf.nn.log_softmax(logits)
new_log_prob = tf.reduce_sum(a * logp_all,
axis=1,
keepdims=True)
entropy = -tf.reduce_mean(
tf.reduce_sum(tf.exp(logp_all) * logp_all,
axis=1,
keepdims=True))
ratio = tf.exp(new_log_prob - old_log_prob)
if self.kl_reverse:
kl = tf.reduce_mean(new_log_prob - old_log_prob)
else:
kl = tf.reduce_mean(
old_log_prob - new_log_prob
) # a sample estimate for KL-divergence, easy to compute
surrogate = ratio * advantage
value_clip = old_value + tf.clip_by_value(
value - old_value, -self.value_epsilon, self.value_epsilon)
td_error = dc_r - value
td_error_clip = dc_r - value_clip
td_square = tf.maximum(tf.square(td_error),
tf.square(td_error_clip))
pi_loss = -tf.reduce_mean(
tf.minimum(
surrogate,
tf.clip_by_value(ratio, 1.0 - self.epsilon,
1.0 + self.epsilon) * advantage))
kl_loss = kl_coef * kl
extra_loss = 1000.0 * tf.square(
tf.maximum(0., kl - self.kl_cutoff))
pi_loss = pi_loss + kl_loss + extra_loss
q_loss = 0.5 * tf.reduce_mean(td_square)
beta_s = tf.reduce_sum(beta * last_options_onehot,
axis=-1,
keepdims=True) # [B, 1]
beta_loss = tf.reduce_mean(beta_s * beta_advantage)
if self.terminal_mask:
beta_loss *= (1 - done)
loss = pi_loss + 1.0 * q_loss + beta_loss - self.pi_beta * entropy
loss_grads = tape.gradient(loss, self.net_tv)
self.optimizer.apply_gradients(zip(loss_grads, self.net_tv))
self.global_step.assign_add(1)
return loss, pi_loss, q_loss, beta_loss, entropy, kl
def train(self, memories, isw, crsty_loss, cell_state):
ss, vvss, a, r, done, old_log_prob = memories
with tf.device(self.device):
with tf.GradientTape() as tape:
feat, feat_ = self.get_feature(ss,
vvss,
cell_state=cell_state,
s_and_s_=True)
if self.is_continuous:
next_mu = self.actor_net(feat_)
max_q_next = tf.stop_gradient(
self.critic_net(feat_, next_mu))
else:
logits = self.actor_net(feat_)
max_a = tf.argmax(logits, axis=1)
max_a_one_hot = tf.one_hot(max_a,
self.a_dim,
dtype=tf.float32)
max_q_next = tf.stop_gradient(
self.critic_net(feat_, max_a_one_hot))
q = self.critic_net(feat, a)
td_error = q - (r + self.gamma * (1 - done) * max_q_next)
critic_loss = tf.reduce_mean(
tf.square(td_error) * isw) + crsty_loss
critic_grads = tape.gradient(critic_loss, self.critic_tv)
self.optimizer_critic.apply_gradients(
zip(critic_grads, self.critic_tv))
with tf.GradientTape() as tape:
if self.is_continuous:
mu = self.actor_net(feat)
log_prob = gaussian_likelihood_sum(a, mu, self.log_std)
entropy = gaussian_entropy(self.log_std)
else:
logits = self.actor_net(feat)
logp_all = tf.nn.log_softmax(logits)
log_prob = tf.reduce_sum(tf.multiply(logp_all, a),
axis=1,
keepdims=True)
entropy = -tf.reduce_mean(
tf.reduce_sum(tf.exp(logp_all) * logp_all,
axis=1,
keepdims=True))
q = self.critic_net(feat, a)
ratio = tf.stop_gradient(tf.exp(log_prob - old_log_prob))
q_value = tf.stop_gradient(q)
actor_loss = -tf.reduce_mean(ratio * log_prob * q_value)
actor_grads = tape.gradient(actor_loss, self.actor_tv)
self.optimizer_actor.apply_gradients(
zip(actor_grads, self.actor_tv))
self.global_step.assign_add(1)
return td_error, dict([['LOSS/actor_loss', actor_loss],
['LOSS/critic_loss', critic_loss],
['Statistics/q_max',
tf.reduce_max(q)],
['Statistics/q_min',
tf.reduce_min(q)],
['Statistics/q_mean',
tf.reduce_mean(q)],
['Statistics/ratio',
tf.reduce_mean(ratio)],
['Statistics/entropy', entropy]])
