def finish_horizon(self, last_val=0):
"""
TODO: These codes are completly identical to the ones defined in on_policy_trainer.py. Use it.
"""
samples = self.local_buffer._encode_sample(
np.arange(self.local_buffer.get_stored_size()))
rews = np.append(samples["rew"], last_val)
vals = np.append(samples["val"], last_val)
# GAE-Lambda advantage calculation
deltas = rews[:-1] + self._policy.discount * vals[1:] - vals[:-1]
if self._policy.enable_gae:
advs = discount_cumsum(deltas,
self._policy.discount * self._policy.lam)
else:
advs = deltas
# Rewards-to-go, to be targets for the value function
rets = discount_cumsum(rews, self._policy.discount)[:-1]
self.replay_buffer.add(obs=samples["obs"],
act=samples["act"],
done=samples["done"],
ret=rets,
adv=advs,
logp=np.squeeze(samples["logp"]))
self.local_buffer.clear()
def finish_horizon(self, last_val=0):
"""
Call this at the end of a trajectory, or when one gets cut off
by an epoch ending. This looks back in the buffer to where the
trajectory started, and uses rewards and value estimates from
the whole trajectory to compute advantage estimates with GAE-Lambda,
as well as compute the rewards-to-go for each state, to use as
the targets for the value function.
The "last_val" argument should be 0 if the trajectory ended
because the agent reached a terminal state (died), and otherwise
should be V(s_T), the value function estimated for the last state.
This allows us to bootstrap the reward-to-go calculation to account
for timesteps beyond the arbitrary episode horizon (or epoch cutoff).
"""
samples = self.local_buffer._encode_sample(
np.arange(self.local_buffer.get_stored_size()))
rews = np.append(samples["rew"], last_val)
vals = np.append(samples["val"], last_val)
# GAE-Lambda advantage calculation
deltas = rews[:-1] + self._policy.discount * vals[1:] - vals[:-1]
if self._policy.enable_gae:
advs = discount_cumsum(
deltas, self._policy.discount * self._policy.lam)
else:
advs = deltas
# Rewards-to-go, to be targets for the value function
rets = discount_cumsum(rews, self._policy.discount)[:-1]
self.replay_buffer.add(
obs=samples["obs"], act=samples["act"], done=samples["done"],
ret=rets, adv=advs, logp=np.squeeze(samples["logp"]))
self.local_buffer.clear()
def test_discount_cumsum(self):
rewards = np.ones(shape=(3, ), dtype=np.float32)
discount = 0.99
expected = np.array(
[1 + discount + discount * discount, 1 + discount, 1],
dtype=np.float32)
results = discount_cumsum(rewards, discount)
np.testing.assert_allclose(expected, results)
def test_discount_cumsum(self):
x = np.array([1., 1., 1.])
discount = 0.99
expected = [
1. + 1.*discount**1 + 1.*discount**2,
1. + 1.*discount**1,
1.]
results = discount_cumsum(x, discount)
np.testing.assert_array_equal(results, expected)