def compute_actions(self,
obs_batch,
state_batches=None,
prev_action_batch=None,
prev_reward_batch=None,
info_batch=None,
episodes=None,
**kwargs):
obs_batch, action_mask = self._unpack_observation(obs_batch)
# Compute actions
with th.no_grad():
q_values, hiddens = _mac(
self.model, th.from_numpy(obs_batch),
[th.from_numpy(np.array(s)) for s in state_batches])
avail = th.from_numpy(action_mask).float()
masked_q_values = q_values.clone()
masked_q_values[avail == 0.0] = -float("inf")
# epsilon-greedy action selector
random_numbers = th.rand_like(q_values[:, :, 0])
pick_random = (random_numbers < self.cur_epsilon).long()
random_actions = Categorical(avail).sample().long()
actions = (pick_random * random_actions +
(1 - pick_random) * masked_q_values.max(dim=2)[1])
actions = actions.numpy()
hiddens = [s.numpy() for s in hiddens]
return TupleActions(list(actions.transpose([1, 0]))), hiddens, {}
def sample(self):
# first, sample a1
a1_dist = self._a1_distribution()
a1 = a1_dist.sample()
# sample a2 conditioned on a1
a2_dist = self._a2_distribution(a1)
a2 = a2_dist.sample()
self._action_logp = a1_dist.logp(a1) + a2_dist.logp(a2)
# return the action tuple
return TupleActions([a1, a2])
def compute_actions(self,
obs_batch,
state_batches=None,
prev_action_batch=None,
prev_reward_batch=None,
info_batch=None,
episodes=None,
**kwargs):
obs_batch, action_mask, _ = self._unpack_observation(obs_batch)
# We need to ensure we do not use the env global state
# to compute actions
# Compute actions
with th.no_grad():
q_values, hiddens = _mac(
self.model,
th.as_tensor(obs_batch, dtype=th.float, device=self.device), [
th.as_tensor(
np.array(s), dtype=th.float, device=self.device)
for s in state_batches
])
avail = th.as_tensor(action_mask,
dtype=th.float,
device=self.device)
masked_q_values = q_values.clone()
masked_q_values[avail == 0.0] = -float("inf")
# epsilon-greedy action selector
random_numbers = th.rand_like(q_values[:, :, 0])
pick_random = (random_numbers < self.cur_epsilon).long()
random_actions = Categorical(avail).sample().long()
actions = (pick_random * random_actions +
(1 - pick_random) * masked_q_values.argmax(dim=2))
actions = actions.cpu().numpy()
hiddens = [s.cpu().numpy() for s in hiddens]
return TupleActions(list(actions.transpose([1, 0]))), hiddens, {}
def sample(self):
return TupleActions([s.sample() for s in self.child_distributions])
def sample(self):
samples = [d.sample() for d in self._distributions]
# print('samples:', samples)
self._last_sample_logp = self._logp(samples)
return TupleActions(samples)
