"""
Policy Gradient (PG)
Paper link: https://proceedings.neurips.cc/paper/2001/file/4b86abe48d358ecf194c56c69108433e-Paper.pdf
Implementation: TensorFlow2
"""
import numpy as np
from argparse import Namespace
from xuance.tensorflow import tf, tk, Module
from xuance.tensorflow.learners import Learner
[docs]
class PG_Learner(Learner):
def __init__(self,
config: Namespace,
policy: Module,
callback):
super(PG_Learner, self).__init__(config, policy, callback)
if ("macOS" in self.os_name) and ("arm" in self.os_name): # For macOS with Apple's M-series chips.
if self.distributed_training:
with self.policy.mirrored_strategy.scope():
self.optimizer = tk.optimizers.legacy.Adam(config.learning_rate)
else:
self.optimizer = tk.optimizers.legacy.Adam(config.learning_rate)
else:
if self.distributed_training:
with self.policy.mirrored_strategy.scope():
self.optimizer = tk.optimizers.Adam(config.learning_rate)
else:
self.optimizer = tk.optimizers.Adam(config.learning_rate)
self.ent_coef = config.ent_coef
self.is_continuous = self.policy.is_continuous
@tf.function
def forward_fn(self, obs_batch, act_batch, ret_batch):
with tf.GradientTape() as tape:
if self.is_continuous:
outputs, mu, std, v_pred = self.policy(obs_batch)
# calculate log prob
log_std = tf.math.log(std + 1e-8)
log_prob = -0.5 * (((act_batch - mu) / (std + 1e-8)) ** 2 + 2.0 * log_std + tf.math.log(2.0 * np.pi))
log_prob_a = tf.reduce_sum(log_prob, axis=-1, keepdims=True)
# calculate entropy
entropy = tf.reduce_sum(0.5 + 0.5 * tf.math.log(2.0 * np.pi) + log_std, axis=-1, keepdims=True)
else:
_, logits, _ = self.policy(obs_batch)
# calculate log prob
log_prob = tf.nn.log_softmax(logits, axis=-1)
log_prob_a = tf.gather(log_prob, act_batch, axis=-1, batch_dims=-1)
# calculate entropy
probs = tf.exp(log_prob)
entropy = -tf.reduce_sum(probs * log_prob, axis=-1, keepdims=True)
a_loss = -tf.reduce_mean(ret_batch * log_prob_a)
e_loss = tf.reduce_mean(entropy)
loss = a_loss - self.ent_coef * e_loss
gradients = tape.gradient(loss, self.policy.trainable_variables)
if self.use_grad_clip:
gradients, _ = tf.clip_by_global_norm(gradients, clip_norm=self.grad_clip_norm)
self.optimizer.apply_gradients(zip(gradients, self.policy.trainable_variables))
else:
self.optimizer.apply_gradients(zip(gradients, self.policy.trainable_variables))
return a_loss, e_loss
@tf.function
def learn(self, *inputs):
if self.distributed_training:
a_loss, e_loss = self.policy.mirrored_strategy.run(self.forward_fn, args=inputs)
return (self.policy.mirrored_strategy.reduce(tf.distribute.ReduceOp.SUM, a_loss, axis=None),
self.policy.mirrored_strategy.reduce(tf.distribute.ReduceOp.SUM, e_loss, axis=None))
else:
return self.forward_fn(*inputs)
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def update(self, **samples):
self.iterations += 1
obs_batch = tf.convert_to_tensor(samples['obs'], dtype=tf.float32)
ret_batch = tf.convert_to_tensor(samples['returns'][:, None], dtype=tf.float32)
if self.is_continuous:
act_batch = tf.convert_to_tensor(samples["actions"], dtype=tf.float32)
else:
act_batch = tf.convert_to_tensor(samples["actions"][:, None], dtype=tf.int32)
info = self.callback.on_update_start(self.iterations,
policy=self.policy, obs=obs_batch, act=act_batch, returns=ret_batch)
a_loss, e_loss = self.learn(obs_batch, act_batch, ret_batch)
info.update({
"actor-loss": a_loss.numpy(),
"entropy": e_loss.numpy()
})
info.update(self.callback.on_update_end(self.iterations, policy=self.policy, info=info,
a_loss=a_loss, e_loss=e_loss))
return info