def fit(self, dataset):
if not self._quiet:
tqdm.write('Iteration ' + str(self._iter))
x, u, r, xn, absorbing, last = parse_dataset(dataset)
x = x.astype(np.float32)
u = u.astype(np.float32)
r = r.astype(np.float32)
xn = xn.astype(np.float32)
obs = to_float_tensor(x, self.policy.use_cuda)
act = to_float_tensor(u, self.policy.use_cuda)
v_target, np_adv = compute_gae(self._V, x, xn, r, absorbing, last, self.mdp_info.gamma, self._lambda)
np_adv = (np_adv - np.mean(np_adv)) / (np.std(np_adv) + 1e-8)
adv = to_float_tensor(np_adv, self.policy.use_cuda)
old_pol_dist = self.policy.distribution_t(obs)
old_log_p = old_pol_dist.log_prob(act)[:, None].detach()
self._V.fit(x, v_target, **self._critic_fit_params)
self._update_policy(obs, act, adv, old_log_p)
# Print fit information
self._print_fit_info(dataset, x, v_target, old_pol_dist)
self._iter += 1
def fit(self, dataset):
if not self._quiet:
tqdm.write('Iteration ' + str(self._iter))
state, action, reward, next_state, absorbing, last = parse_dataset(
dataset)
x = state.astype(np.float32)
u = action.astype(np.float32)
r = reward.astype(np.float32)
xn = next_state.astype(np.float32)
obs = to_float_tensor(x, self.policy.use_cuda)
act = to_float_tensor(u, self.policy.use_cuda)
v_target, np_adv = compute_gae(self._V, x, xn, r, absorbing, last,
self.mdp_info.gamma, self._lambda)
np_adv = (np_adv - np.mean(np_adv)) / (np.std(np_adv) + 1e-8)
adv = to_float_tensor(np_adv, self.policy.use_cuda)
# Policy update
self._old_policy = deepcopy(self.policy)
old_pol_dist = self._old_policy.distribution_t(obs)
old_log_prob = self._old_policy.log_prob_t(obs, act).detach()
zero_grad(self.policy.parameters())
loss = self._compute_loss(obs, act, adv, old_log_prob)
prev_loss = loss.item()
# Compute Gradient
loss.backward()
g = get_gradient(self.policy.parameters())
# Compute direction through conjugate gradient
stepdir = self._conjugate_gradient(g, obs, old_pol_dist)
# Line search
self._line_search(obs, act, adv, old_log_prob, old_pol_dist, prev_loss,
stepdir)
# VF update
self._V.fit(x, v_target, **self._critic_fit_params)
# Print fit information
self._print_fit_info(dataset, x, v_target, old_pol_dist)
self._iter += 1