def forward(
self,
obs,
deterministic=False,
return_log_prob=False,
return_entropy=False,
return_log_prob_of_mean=False,
):
"""
:param obs: Observation
:param deterministic: If True, do not sample
:param return_log_prob: If True, return a sample and its log probability
:param return_entropy: If True, return the true expected log
prob. Will not need to be differentiated through, so this can be a
number.
:param return_log_prob_of_mean: If True, return the true expected log
prob. Will not need to be differentiated through, so this can be a
number.
"""
obs, taus = split_tau(obs)
batch_size = obs.size()[0]
tau_vector = Variable(
torch.zeros((batch_size, self.tau_vector_len)) + taus.data)
h = obs
h1 = self.hidden_activation(self.first_input(h))
h2 = self.hidden_activation(self.second_input(tau_vector))
h = torch.cat((h1, h2), dim=1)
for i, fc in enumerate(self.fcs):
h = self.hidden_activation(fc(h))
mean = self.last_fc(h)
if self.std is None:
log_std = self.last_fc_log_std(h)
log_std = torch.clamp(log_std, LOG_SIG_MIN, LOG_SIG_MAX)
std = torch.exp(log_std)
else:
std = self.std
log_std = self.log_std
log_prob = None
entropy = None
mean_action_log_prob = None
pre_tanh_value = None
if deterministic:
action = torch.tanh(mean)
else:
tanh_normal = TanhNormal(mean, std)
if return_log_prob:
action, pre_tanh_value = tanh_normal.sample(
return_pretanh_value=True)
log_prob = tanh_normal.log_prob(action,
pre_tanh_value=pre_tanh_value)
log_prob = log_prob.sum(dim=1, keepdim=True)
else:
action = tanh_normal.sample()
if return_entropy:
entropy = log_std + 0.5 + np.log(2 * np.pi) / 2
# Because tanh is invertible, the entropy of a Gaussian and the
# entropy of the tanh of a Gaussian is the same.
entropy = entropy.sum(dim=1, keepdim=True)
if return_log_prob_of_mean:
tanh_normal = TanhNormal(mean, std)
mean_action_log_prob = tanh_normal.log_prob(
torch.tanh(mean),
pre_tanh_value=mean,
)
mean_action_log_prob = mean_action_log_prob.sum(dim=1,
keepdim=True)
return (
action,
mean,
log_std,
log_prob,
entropy,
std,
mean_action_log_prob,
pre_tanh_value,
)
def forward(
self,
observation,
state,
actions=None,
reparameterize=True,
deterministic=False,
return_log_prob=False,
):
# import ipdb; ipdb.set_trace()
tensor = observation
# normalize
# tensor = torch.div(tensor, 255.)
for layer in self.convs:
tensor = layer(tensor)
tensor = self.conv_activation(tensor)
tensor = batch_flatten(tensor)
if state is not None:
tensor = torch.cat((tensor, state), 1)
i = 0
for layer in self.fcs:
h = tensor
tensor = layer(tensor)
if i == len(self.fcs) - 1:
tensor = self.output_activation(tensor)
else:
tensor = self.fcs_activation(tensor)
i += 1
mean = tensor
log_std = self.last_fc_log_std(h)
log_std = torch.clamp(log_std, LOG_SIG_MIN, LOG_SIG_MAX)
std = torch.exp(log_std)
mean = torch.clamp(mean, LOG_MEAN_MIN, LOG_MEAN_MAX)
tanh_normal = TanhNormal(mean, std)
log_prob = None
entropy = None
mean_action_log_prob = None
pre_tanh_value = None
if deterministic:
action = torch.tanh(mean)
else:
tanh_normal = TanhNormal(mean, std)
if return_log_prob:
if reparameterize is True:
action, pre_tanh_value = tanh_normal.rsample(
return_pretanh_value=True)
else:
action, pre_tanh_value = tanh_normal.sample(
return_pretanh_value=True)
log_prob = tanh_normal.log_prob(action,
pre_tanh_value=pre_tanh_value)
log_prob = log_prob.sum(dim=1, keepdim=True)
else:
if reparameterize is True:
action = tanh_normal.rsample()
else:
action = tanh_normal.sample()
return (
action,
mean,
log_std,
log_prob,
entropy,
std,
mean_action_log_prob,
pre_tanh_value,
)
def forward(
self,
obs,
reparameterize=True,
deterministic=False,
return_log_prob=False,
return_entropy=False,
return_log_prob_of_mean=False,
):
"""
:param obs: Observation
:param deterministic: If True, do not sample
:param return_log_prob: If True, return a sample and its log probability
:param return_entropy: If True, return the true expected log
prob. Will not need to be differentiated through, so this can be a
number.
:param return_log_prob_of_mean: If True, return the true expected log
prob. Will not need to be differentiated through, so this can be a
number.
"""
h = obs
for i, fc in enumerate(self.fcs):
h = self.hidden_activation(fc(h))
mean = self.last_fc(h)
if self.std is None:
log_std = self.last_fc_log_std(h)
log_std = torch.clamp(log_std, LOG_SIG_MIN, LOG_SIG_MAX)
std = torch.exp(log_std)
else:
std = self.std
log_std = self.log_std
log_prob = None
entropy = None
mean_action_log_prob = None
pre_tanh_value = None
if deterministic:
action = torch.tanh(mean)
else:
tanh_normal = TanhNormal(mean, std)
if return_log_prob:
if reparameterize is True:
action, pre_tanh_value = tanh_normal.rsample(
return_pretanh_value=True)
else:
action, pre_tanh_value = tanh_normal.sample(
return_pretanh_value=True)
log_prob = tanh_normal.log_prob(action,
pre_tanh_value=pre_tanh_value)
log_prob = log_prob.sum(dim=1, keepdim=True)
else:
if reparameterize is True:
action = tanh_normal.rsample()
else:
action = tanh_normal.sample()
if return_entropy:
entropy = log_std + 0.5 + np.log(2 * np.pi) / 2
# I'm not sure how to compute the (differential) entropy for a
# tanh(Gaussian)
entropy = entropy.sum(dim=1, keepdim=True)
raise NotImplementedError()
if return_log_prob_of_mean:
tanh_normal = TanhNormal(mean, std)
mean_action_log_prob = tanh_normal.log_prob(
torch.tanh(mean),
pre_tanh_value=mean,
)
mean_action_log_prob = mean_action_log_prob.sum(dim=1,
keepdim=True)
return (
action,
mean,
log_std,
log_prob,
entropy,
std,
mean_action_log_prob,
pre_tanh_value,
)