def rollout(self, timestep_limit, add_noise=True):
rollout_rewards, rollout_fragment_length = rollout(
self.policy,
self.env,
timestep_limit=timestep_limit,
add_noise=add_noise)
return rollout_rewards, rollout_fragment_length
def rollout(self, timestep_limit):
rollout_rewards, rollout_length = rollout(
self.policy,
self.env,
timestep_limit=timestep_limit,
add_noise=False)
return rollout_rewards, rollout_length
def rollout(self, timestep_limit, add_noise=False):
rollout_rewards, rollout_fragment_length = rollout(
self.policy,
self.env,
timestep_limit=timestep_limit,
add_noise=add_noise,
offset=self.config["offset"])
return rollout_rewards, rollout_fragment_length
def evaluate(self, candidate):
noise_index, multiplier = candidate
weights = self.common.model_keeper.get_perturbed_weights(
noise_index, multiplier)
self.common.policy.set_flat_weights(weights)
rewards, length = \
rollout(
self.common.policy,
self.common.env,
timestep_limit=self.timestep_limit,
add_noise=False)
return rewards.sum(), length
def evaluate(self, candidate):
# ******************************* how to evaluate a candidate message
weights = self.common.optimizer.expand(candidate)
self.common.policy.set_flat_weights(weights)
rewards, length = \
rollout(
self.common.policy,
self.common.env,
timestep_limit=self.timestep_limit,
add_noise=False)
# if candidate == 0:
logger.info('candidate {} {} {} {} {}'.format(
candidate, weights[0],
self.common.policy.get_flat_weights()[0], rewards.sum(), length))
return rewards.sum(), length
