def postprocess_trajectory(self,
sample_batch,
other_agent_batches=None,
episode=None):
if self.config["parameter_noise"]:
# adjust the sigma of parameter space noise
states, noisy_actions = [
list(x) for x in sample_batch.columns(["obs", "actions"])
]
self.sess.run(self.remove_noise_op)
clean_actions = self.sess.run(
self.output_actions,
feed_dict={
self.cur_observations: states,
self.stochastic: False,
self.eps: .0
})
distance_in_action_space = np.sqrt(
np.mean(np.square(clean_actions - noisy_actions)))
self.pi_distance = distance_in_action_space
if distance_in_action_space < self.config["exploration_sigma"]:
self.parameter_noise_sigma_val *= 1.01
else:
self.parameter_noise_sigma_val /= 1.01
self.parameter_noise_sigma.load(
self.parameter_noise_sigma_val, session=self.sess)
return _postprocess_dqn(self, sample_batch)
def postprocess_trajectory(self,
sample_batch,
other_agent_batches=None,
episode=None):
if self.config["parameter_noise"]:
# adjust the sigma of parameter space noise
states, noisy_actions = [
list(x) for x in sample_batch.columns(["obs", "actions"])
]
self.sess.run(self.remove_noise_op)
clean_actions = self.sess.run(self.output_actions,
feed_dict={
self.cur_observations: states,
self.stochastic: False,
self.eps: .0
})
distance_in_action_space = np.sqrt(
np.mean(np.square(clean_actions - noisy_actions)))
self.pi_distance = distance_in_action_space
if distance_in_action_space < self.config["exploration_sigma"]:
self.parameter_noise_sigma_val *= 1.01
else:
self.parameter_noise_sigma_val /= 1.01
self.parameter_noise_sigma.load(self.parameter_noise_sigma_val,
session=self.sess)
return _postprocess_dqn(self, sample_batch)
def postprocess_trajectory(self, sample_batch, other_agent_batches=None):
return _postprocess_dqn(self, sample_batch)
def postprocess_trajectory(self,
sample_batch,
other_agent_batches=None,
episode=None):
return _postprocess_dqn(self, sample_batch)