"""
Deep Recurrent Q-Netwrk (DRQN)
Paper link: https://cdn.aaai.org/ocs/11673/11673-51288-1-PB.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 DRQN_Learner(Learner):
def __init__(self,
config: Namespace,
policy: Module,
callback):
super(DRQN_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.gamma = config.gamma
self.sync_frequency = config.sync_frequency
self.n_actions = self.policy.action_dim
self.mse_loss = tk.losses.MeanSquaredError()
@tf.function
def forward_fn(self, batch_size, obs_batch, act_batch, rew_batch, ter_batch):
with tf.GradientTape() as tape:
rnn_hidden = self.policy.init_hidden(batch_size)
_, _, evalQ, _ = self.policy(obs_batch[:, 0:-1], *rnn_hidden)
target_rnn_hidden = self.policy.init_hidden(batch_size)
_, targetA, targetQ, _ = self.policy.target(obs_batch[:, 1:], *target_rnn_hidden)
# targetQ = targetQ.max(dim=-1).values
targetA = tf.one_hot(targetA, targetQ.shape[-1])
targetQ = tf.reduce_mean(targetQ * targetA, axis=-1)
targetQ = rew_batch + self.gamma * (1 - ter_batch) * targetQ
predictQ = tf.reduce_mean(evalQ * tf.one_hot(act_batch, evalQ.shape[-1]), axis=-1)
targetQ = tf.reshape(targetQ, [-1])
predictQ = tf.reshape(predictQ, [-1])
loss = self.mse_loss(targetQ, predictQ)
gradients = tape.gradient(loss, self.policy.trainable_variables)
if self.use_grad_clip:
self.optimizer.apply_gradients([
(tf.clip_by_norm(grad, self.grad_clip_norm), var)
for (grad, var) in zip(gradients, self.policy.trainable_variables)
if grad is not None
])
else:
self.optimizer.apply_gradients([
(grad, var)
for (grad, var) in zip(gradients, self.policy.trainable_variables)
if grad is not None
])
return predictQ, loss
@tf.function
def learn(self, *inputs):
if self.distributed_training:
predictQ, loss = self.policy.mirrored_strategy.run(self.forward_fn, args=inputs)
return (self.policy.mirrored_strategy.reduce(tf.distribute.ReduceOp.SUM, predictQ, axis=None),
self.policy.mirrored_strategy.reduce(tf.distribute.ReduceOp.SUM, loss, axis=None))
else:
return self.forward_fn(*inputs)
[docs]
def update(self, **samples):
self.iterations += 1
obs_batch = samples['obs']
act_batch = samples['actions'].astype(np.int32)
rew_batch = samples['rewards']
ter_batch = samples['terminals'].astype(np.float32)
batch_size = obs_batch.shape[0]
info = self.callback.on_update_start(self.iterations,
policy=self.policy, obs=obs_batch, act=act_batch,
rew=rew_batch, termination=ter_batch, batch_size=batch_size)
predictQ, loss = self.learn(batch_size, obs_batch, act_batch, rew_batch, ter_batch)
# hard update for target network
if self.iterations % self.sync_frequency == 0:
self.policy.copy_target()
info.update({
"Qloss": loss.numpy(),
"predictQ": tf.math.reduce_mean(predictQ).numpy()
})
info.update(self.callback.on_update_end(self.iterations, policy=self.policy, info=info,
predictQ=predictQ, loss=loss))
return info