def finish_path(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).
"""
path_slice = slice(self.path_start_idx, self.ptr)
rews = np.append(self.rew_buf[path_slice], last_val)
vals = np.append(self.val_buf[path_slice], last_val)
# the next two lines implement GAE-Lambda advantage calculation
deltas = rews[:-1] + self.gamma * vals[1:] - vals[:-1]
self.adv_buf[path_slice] = core.discount_cumsum(
deltas, self.gamma * self.lam)
# the next line computes rewards-to-go, to be targets for the value function
self.ret_buf[path_slice] = core.discount_cumsum(rews, self.gamma)[:-1]
self.path_start_idx = self.ptr
def finish_path(self, last_val=0):
self.rew_buf.append(last_val)
self.val_buf.append(last_val)
rews = np.array(self.rew_buf)
vals = np.array(self.val_buf)
# the next two lines implement GAE-Lambda advantage calculation
deltas = rews[:-1] + self.gamma * vals[1:] - vals[:-1]
adv_buf = core.discount_cumsum(deltas, self.gamma * self.lam)
# the next line computes rewards-to-go, to be targets for the value function
ret_buf = core.discount_cumsum(rews, self.gamma)[:-1]
trajectory = [dict(obs=obs, act=act, ret=ret, adv=adv, logp=logp) \
for obs, act, ret, adv, logp in zip(self.obs_buf, self.act_buf, ret_buf, adv_buf, self.logp_buf)]
self.reset()
""" TODO: Advantage normalization """
return trajectory
def finish_path(self, agent_index, 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 estimate the reward-to-go calculation via bootstrapping
to account for timesteps beyond the arbitrary episode horizon
(or epoch cutoff).
"""
i = agent_index
path_slice = slice(self.path_start_idx[i], self.ptr[i])
rews = np.append(self.rew_buf[i][path_slice], last_val)
vals = np.append(self.val_buf[i][path_slice], last_val)
# the next two lines implement GAE-Lambda advantage calculation
deltas = rews[:-1] + self.gamma * vals[1:] - vals[:-1]
self.adv_buf[i][path_slice] = core.discount_cumsum(
deltas, self.gamma * self.lam)
if self.shift_advs_pct:
advs = self.adv_buf[i][path_slice]
# TODO: Tie this to Entropy
# 90 seems to work speed up convergence whereas 99 and 99.9 slow it down
# perhaps something to do with size of 1 standard deviation
self.adv_buf[i][path_slice] = advs - np.percentile(advs, self.shift_advs_pct)
# the next line computes rewards-to-go, to be targets for the value function
self.ret_buf[i][path_slice] = core.discount_cumsum(rews, self.gamma)[:-1]
self.path_start_idx[i] = self.ptr[i]
