class DDPG(object):
"""deep deterministic policy gradient
"""
def __init__(self,
n_state,
n_action,
a_bound,
gamma=0.99,
tau=0.01,
actor_lr=0.0005,
critic_lr=0.001,
noise_std=0.1,
noise_decay=0.9995,
noise_decay_steps=1000,
buffer_size=20000,
save_interval=5000,
assess_interval=10,
logger=None,
checkpoint_queen=None):
self.logger = logger
self.logger.save_config(locals())
self.n_action = n_action
self.n_state = n_state
self.a_bound = a_bound
self.noise_std = noise_std
self.noise_decay = noise_decay
self.noise_decay_steps = noise_decay_steps
self.pointer = 0
self.buffer_size = buffer_size
self.save_interval = save_interval
self.assess_interval = assess_interval
self.actor = Actor(self.n_state,
self.n_action,
gamma=gamma,
lr=actor_lr,
tau=tau,
l2_reg=0)
self.critic = Critic(self.n_state,
self.n_action,
gamma=gamma,
lr=critic_lr,
tau=tau,
l2_reg=0)
self.merge = self._merge_summary()
self.ckpt_queen = checkpoint_queen
self.prefix = self.__class__.__name__.lower()
def _merge_summary(self):
tf.summary.histogram('critic_output', self.critic.model.output)
tf.summary.histogram('actor_output', self.actor.model.output)
tf.summary.histogram('critic_dense1',
self.critic.model.get_layer('l1').weights[0])
tf.summary.histogram('actor_dense1',
self.actor.model.get_layer('l1').weights[0])
tf.summary.histogram('critic_dense2',
self.critic.model.get_layer('l2').weights[0])
tf.summary.histogram('actor_dense2',
self.actor.model.get_layer('l2').weights[0])
return tf.summary.merge_all()
def policy_action(self, state):
return self.actor.predict(state)
def bellman_q_value(self, rewards, q_nexts, dones):
""" Use the Bellman Equation to compute the critic target
"""
q_target = np.zeros_like(
rewards) # asarry( copy = False), array(cope=True)
for i in range(rewards.shape[0]):
if dones[i]:
q_target[i] = rewards[i]
else:
q_target[i] = rewards[i] + self.critic.gamma * q_nexts[i]
return q_target
def update_model(self, states, actions, q_values):
# train critic
loss_names, loss_values = self.critic.train_on_batch(
states, actions, q_values)
# train actor
# p_actions = self.actor.predict(states) #actions with no noise
grad_ys = self.critic.gradients(
states, self.actor.predict(states)) #(batch, n-action)
actor_output = self.actor.train(states, self.actor.predict(states),
grad_ys)
# copy network
self.actor.copy_weights()
self.critic.copy_weights()
# print(grad_ys, grad_ys.shape)
# print(actor_output[0],actor_output[0].shape)
# print(np.mean(grad_ys*actor_output[0]))
return loss_names, loss_values, grad_ys, actor_output
def save_weights(self, path):
self.actor.save(path)
self.critic.save(path)
def save_model(self, path, file):
self.actor.model.save(
os.path.join(path, self.prefix + '_actor_' + file + '.h5'))
self.critic.model.save(
os.path.join(path, self.prefix + '_critic_' + file + '.h5'))
def checkpoint(self, path, step, metric_value):
signature = str(step) + '_' + '{:.4f}'.format(metric_value)
to_delete, need_save = self.ckpt_queen.add((metric_value, signature))
if to_delete:
actor = os.path.join(
path, self.prefix + '_actor_' + to_delete[1] + '.h5')
critic = os.path.join(
path, self.prefix + '_critic_' + to_delete[1] + '.h5')
os.remove(actor)
os.remove(critic)
if need_save:
self.save_model(path, signature)
def train(self,
args,
summary_writer,
train_data=None,
val_data=None,
test_data=None):
results = []
max_val_rate = 0
val_data = np.asarray(val_data) # none will be array(None)
# First, gather experience
tqdm_e = tqdm(range(args.batchs),
desc='score',
leave=True,
unit=" epoch")
if train_data is None:
dataset = CsvBuffer(args.file_dir,
args.reg_pattern,
chunksize=args.batch_size) # 100*(20+1)
assert dataset.is_buffer_available, 'neither train_data nor csv buffer is available'
# noise = OrnsteinUhlenbeckProcess(size=self.n_action)
else:
dataset = Dataset(train_data, args.batch_size, shuffle=True)
for e in tqdm_e:
batch_data = next(dataset)
states, labels = batch_data[:, :-1], batch_data[:, -1].astype(int)
a = self.policy_action(states) #(batch, n_action)
a = np.clip(a + np.random.normal(0, self.noise_std, size=a.shape),
self.a_bound[0], self.a_bound[1])
# a = np.clip(np.random.normal(a, self.noise_std), self.a_bound[0], self.a_bound[1])
# a = np.clip(a + noise.generate(time, a.shape[0]), self.a_bound[0], self.a_bound[1])
llr = np.clip(np.log(a / (1 - a) + 1e-6), -5, 5)
r = np.where(labels == 1, llr.ravel(), -llr.ravel()) #(batch,)
# q_nexts = self.critic.target_predict(new_states, self.actor.target_predict(new_states))
q_ = self.bellman_q_value(rewards=r,
q_nexts=0,
dones=[True] * r.shape[0]) #(batch,)
loss_names, loss_values, grad_ys, actor_output = self.update_model(
states, a, q_.reshape(-1, 1))
score = r.mean()
if ((e + 1) % self.noise_decay_steps - 1) == 0:
self.noise_std *= self.noise_decay
self.logger.log_tabular('noise', self.noise_std)
if e % self.assess_interval == 0 or e == args.batchs - 1:
if val_data is not None:
val_pred = self.actor.predict(val_data[:, :-1])
val_y = val_data[:, -1]
val_rate, top_k = top_ratio_hit_rate(
val_y.ravel(), val_pred.ravel())
self.logger.log_tabular('val_rate', val_rate)
self.logger.log_tabular('val_k', int(top_k))
self.checkpoint(args.model_path, e, val_rate)
max_val_rate = val_rate if val_rate > max_val_rate else max_val_rate
if test_data is not None:
test_pred = self.actor.predict(test_data[:, :-1])
test_y = test_data[:, -1]
test_rate, top_k = top_ratio_hit_rate(
test_y, test_pred.ravel())
self.logger.log_tabular('test_rate', test_rate)
self.logger.log_tabular('test_k', int(top_k))
summary_writer.add_summary(tf_summary(['mean-reward'],
[score]),
global_step=e)
summary_writer.add_summary(tf_summary(loss_names,
[loss_values]),
global_step=e)
merge = keras.backend.get_session().run(
self.merge,
feed_dict={
self.critic.model.input[0]: states,
self.critic.model.input[1]: a,
self.actor.model.input: states
})
summary_writer.add_summary(merge, global_step=e)
for name, val in zip(loss_names, [loss_values]):
self.logger.log_tabular(name, val)
# print(grad_ys,grad_ys.shape)
# print(actor_output)
self.logger.log_tabular(
'dQ/da', '%.4f+%.4f' %
(grad_ys.mean(), grad_ys.std())) # grad_ys (batch,act_dim)
self.logger.log_tabular(
'aout',
'%.4f+%.4f' % (actor_output[0].mean(), actor_output[0].std()))
self.logger.log_tabular('aloss', '%.4f' % (actor_output[1]))
self.logger.log_tabular('reward', '%.4f+%.4f' % (score, r.std()))
self.logger.dump_tabular()
tqdm_e.set_description("score: " + '{:.4f}'.format(score))
tqdm_e.set_postfix(noise_std='{:.4f}'.format(self.noise_std),
max_val_rate='{:.4f}'.format(max_val_rate),
val_rate='{:.4f}'.format(val_rate),
top_k=top_k)
tqdm_e.refresh()
return results
class TD3(object):
"""deep deterministic policy gradient
"""
def __init__(self,
n_state,
n_action,
a_bound,
discount=0.99,
tau=0.05,
actor_lr=0.001,
critic_lr=0.001,
policy_freq=2,
exp_noise_std=0.1,
noise_decay=0.9995,
noise_decay_steps=1000,
smooth_noise_std=0.1,
clip=0.2,
buffer_size=20000,
save_interval=5000,
assess_interval=20,
logger=None,
checkpoint_queen=None):
#self.__dict__.update(locals())
self.logger = logger
self.logger.save_config(locals())
self.n_action = n_action
self.n_state = n_state
self.a_bound = a_bound
self.noise_std = exp_noise_std
self.noise_decay = noise_decay
self.noise_decay_steps = noise_decay_steps
self.policy_freq = policy_freq
self.smooth_noise_std = smooth_noise_std
self.clip = clip
self.discount = discount
self.pointer = 0
self.buffer = MemoryBuffer(buffer_size, with_per=True)
self.save_interval = save_interval
self.assess_interval = assess_interval
self.actor = Actor(self.n_state,
self.n_action,
gamma=discount,
lr=actor_lr,
tau=tau)
self.critic1 = Critic(self.n_state,
self.n_action,
gamma=discount,
lr=critic_lr,
tau=tau)
self.critic2 = Critic(self.n_state,
self.n_action,
gamma=discount,
lr=critic_lr,
tau=tau)
self.merge = self._merge_summary()
self.ckpt_queen = checkpoint_queen
self.prefix = self.__class__.__name__
def _merge_summary(self):
tf.summary.histogram('critic_output', self.critic1.model.output)
tf.summary.histogram('actor_output', self.actor.model.output)
tf.summary.histogram('critic_dense1',
self.critic1.model.get_layer('l1').weights[0])
tf.summary.histogram('actor_dense1',
self.actor.model.get_layer('l1').weights[0])
tf.summary.histogram('critic_dense2',
self.critic1.model.get_layer('l2').weights[0])
tf.summary.histogram('actor_dense2',
self.actor.model.get_layer('l2').weights[0])
return tf.summary.merge_all()
def select_action(self, state):
return self.actor.predict(state)
def bellman_q_value(self, rewards, q_nexts, dones):
""" Use the Bellman Equation to compute the critic target
"""
q_target = np.zeros_like(
rewards) #asarry( copy = False), array(cope=True)
for i in range(rewards.shape[0]):
if dones[i]:
q_target[i] = rewards[i]
else:
q_target[i] = rewards[i] + self.discount * q_nexts[i]
return q_target
def memorize(self, state, action, reward, done, new_state):
""" Store experience in memory buffer
"""
if (self.buffer.with_per):
q_val = reward
q_val_t = self.critic1.target_predict(state, action)
td_error = abs(q_val_t - q_val)[0]
# print(td_error)
else:
td_error = 0
state = state.reshape(-1)
action = action.reshape(-1)
self.buffer.memorize(state, action, reward, done, new_state, td_error)
def sample_batch(self, batch_size):
return self.buffer.sample_batch(batch_size)
def update_actor(self, states):
actions = self.actor.predict(states)
grad_ys = self.critic1.gradients(states, actions)
actor_output = self.actor.train(states, actions, grad_ys)
self.actor.copy_weights()
self.critic1.copy_weights()
self.critic2.copy_weights()
return grad_ys, actor_output
def update_critic(self, states, actions, q_values):
loss_names, loss_values = self.critic1.train_on_batch(
states, actions, q_values)
self.critic2.train_on_batch(states, actions, q_values)
return loss_names, loss_values
def save_weights(self, path):
self.actor.save(path)
self.critic1.save(path)
self.critic2.save(path)
def save_model(self, path, file):
self.actor.model.save(
os.path.join(path, self.prefix + '_actor_' + file + '.h5'))
self.critic1.model.save(
os.path.join(path, self.prefix + '_critic1_' + file + '.h5'))
self.critic2.model.save(
os.path.join(path, self.prefix + '_critic2_' + file + '.h5'))
def checkpoint(self, path, step, metric_value):
signature = str(step) + '_' + '{:.4}'.format(metric_value)
to_delete, need_save = self.ckpt_queen.add((metric_value, signature))
if to_delete:
delete_actor = os.path.join(
path, self.prefix + '_actor_' + to_delete[1] + '.h5')
delete_critic1 = os.path.join(
path, self.prefix + '_critic1_' + to_delete[1] + '.h5')
delete_critic2 = os.path.join(
path, self.prefix + '_critic2_' + to_delete[1] + '.h5')
os.remove(delete_actor)
os.remove(delete_critic1)
os.remove(delete_critic2)
if need_save:
self.save_model(path, signature)
def train(self,
args,
summary_writer,
train_data=None,
val_data=None,
test_data=None):
results = []
max_val_rate = 0
val_data = np.asarray(val_data) # none will be array(None)
# First, gather experience
tqdm_e = tqdm(range(args.batchs),
desc='score',
leave=True,
unit="epoch")
if train_data is None:
dataset = CsvBuffer(args.file_dir,
args.reg_pattern,
chunksize=args.batch_size) # 100*(20+1)
assert dataset.is_buffer_available, 'neither train_data nor csv buffer is available'
# noise = OrnsteinUhlenbeckProcess(size=self.n_action)
else:
dataset = Dataset(train_data, 1, shuffle=True)
warm_up = 20 * args.batch_size
for e in tqdm_e:
batch_data = next(dataset)
states, labels = batch_data[:, :-1], batch_data[:, -1].astype(int)
a = self.select_action(states) #(batch, n_action)
a = np.clip(a + np.random.normal(0, self.noise_std, size=a.shape),
self.a_bound[0], self.a_bound[1])
llr = np.clip(np.log(a / (1 - a) + 1e-6), -5, 5)
# rewards = np.where(labels==1, llr.ravel(), -llr.ravel()) #(batch,)
rewards = np.where(labels == 1,
np.where(llr > 0, llr.ravel(), 2 * llr.ravel()),
np.where(llr < 0, -llr.ravel(),
-2 * llr.ravel())) #(batch,)
# print(rewards)
# a_ = self.actor.target_predict(next_states)
# noise = np.clip(np.random.normal(0, self.smooth_noise_std), 0, self.clip)
# a_ = a_ + noise
# q_next1 = self.critic1.target_predict(new_states, a_)
# q_next2 = self.critic2.target_predict(new_states,a_)
# q_nexts = np.where(q_next1<q_next2, q_next1, q_next2)
self.memorize(states, a, rewards, True, None)
if e < warm_up:
continue
states, a, rewards, _, _, _ = self.sample_batch(args.batch_size)
# print(states.shape, a.shape, rewards.shape)
q_ = self.bellman_q_value(rewards=rewards,
q_nexts=0,
dones=[True] *
rewards.shape[0]) #(batch,)
loss_names, loss_values = self.update_critic(
states, a, q_.reshape(-1, 1))
if e % self.policy_freq == 0 or e == warm_up:
grad_ys, actor_output = self.update_actor(states)
if ((e + 1) % self.noise_decay_steps - 1) == 0 or e == warm_up:
self.noise_std *= self.noise_decay
self.logger.log_tabular('noise', self.noise_std)
if e % self.assess_interval == 0 or e == args.batchs - 1 or e == warm_up:
if val_data is not None:
val_pred = self.actor.predict(val_data[:, :-1])
val_y = val_data[:, -1]
# print(val_pred.shape,val_pred[:10])
# print(val_y.shape, val_y[:10])
val_rate, top_k = top_ratio_hit_rate(
val_y.ravel(), val_pred.ravel())
self.logger.log_tabular('val_rate', val_rate)
self.logger.log_tabular('val_k', int(top_k))
self.checkpoint(args.model_path, e, val_rate)
max_val_rate = val_rate if val_rate > max_val_rate else max_val_rate
if test_data is not None:
test_pred = self.actor.predict(test_data[:, :-1])
test_y = test_data[:, -1]
test_rate, top_k = top_ratio_hit_rate(
test_y, test_pred.ravel())
self.logger.log_tabular('test_rate', test_rate)
self.logger.log_tabular('test_k', int(top_k))
score = rewards.mean()
summary_writer.add_summary(tf_summary(['mean-reward'], [score]),
global_step=e)
summary_writer.add_summary(tf_summary(loss_names, [loss_values]),
global_step=e)
merge = keras.backend.get_session().run(
self.merge,
feed_dict={
self.critic1.model.input[0]: states,
self.critic1.model.input[1]: a,
self.actor.model.input: states
})
summary_writer.add_summary(merge, global_step=e)
for name, val in zip(loss_names, [loss_values]):
self.logger.log_tabular(name, val)
self.logger.log_tabular(
'dQ/da', '%.4f+%.4f' %
(grad_ys.mean(), grad_ys.std())) # grad_ys (batch,act_dim)
self.logger.log_tabular(
'aout',
'%.4f+%.4f' % (actor_output[0].mean(), actor_output[0].std()))
self.logger.log_tabular('aloss', '%.4f' % (actor_output[1]))
self.logger.log_tabular('reward',
'%.4f+%.4f' % (score, rewards.std()))
self.logger.dump_tabular()
tqdm_e.set_description("score: " + '{:.4f}'.format(score))
tqdm_e.set_postfix(noise_std='{:.4}'.format(self.noise_std),
max_val_rate='{:.4}'.format(max_val_rate),
val_rate='{:.4}'.format(val_rate),
top_k=top_k)
tqdm_e.refresh()
return results
