def choose_action(self, observation):
mu, sigma = self.actor.forward(observation)#.to(self.actor.device)
sigma = T.exp(sigma)
action_probs = Independent(Normal(mu, sigma),1)
probs = action_probs.sample()
self.log_probs = action_probs.log_prob(probs).to(self.actor.device)
return probs
def forward( # type: ignore
self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
input: str = "obs",
**kwargs: Any,
) -> Batch:
obs = batch[input]
logits, h = self.actor(obs, state=state, info=batch.info)
assert isinstance(logits, tuple)
dist = Independent(Normal(*logits), 1)
if self._deterministic_eval and not self.training:
x = logits[0]
else:
x = dist.rsample()
y = torch.tanh(x)
act = y * self._action_scale + self._action_bias
y = self._action_scale * (1 - y.pow(2)) + self.__eps
log_prob = dist.log_prob(x).unsqueeze(-1)
log_prob = log_prob - torch.log(y).sum(-1, keepdim=True)
if self._noise is not None and self.training and not self.updating:
act += to_torch_as(self._noise(act.shape), act)
act = act.clamp(self._range[0], self._range[1])
return Batch(
logits=logits, act=act, state=h, dist=dist, log_prob=log_prob)
def forward(self, input, kl_coef): b, m, train_m, test_m = input mean, std = self.ode_rnn(input) # (batch_size, LO_hidden_size) * 2 d = Normal(torch.tensor([0.0], device = self.param['device']), torch.tensor([1.0], device = self.param['device'])) r = d.sample(mean.shape).squeeze(-1) z0 = mean + r * std z_out = odeint(self.ode_func, z0, b[0, :, 0], rtol = self.param['rtol'], atol = self.param['atol']) # (num_time_points, batch_size, LO_hidden_size) z_out = z_out.permute(1, 0, 2) output = self.output_output(z_out).squeeze(2) z0_distr = Normal(mean, std) kl_div = kl_divergence(z0_distr, Normal(torch.tensor([0.0], device = self.param['device']), torch.tensor([1.0], device = self.param['device']))) kl_div = kl_div.mean(axis = 1) masked_output = output[test_m.bool()].reshape(self.param['batch_size'], (self.param['total_points'] - self.param['obs_points'])) target = b[:, :, 1][test_m.bool()].reshape(self.param['batch_size'], (self.param['total_points'] - self.param['obs_points'])) gaussian = Independent(Normal(loc = masked_output, scale = self.param['obsrv_std']), 1) log_prob = gaussian.log_prob(target) likelihood = log_prob / output.shape[1] loss = -torch.logsumexp(likelihood - kl_coef * kl_div, 0) mse = self.mse_func(masked_output, target) return loss, mse, masked_output
class IndependentNormal(Distribution):
arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}
support = constraints.positive
has_rsample = True
def __init__(self, loc, scale, validate_args=None):
self.base_dist = Independent(Normal(loc=loc,
scale=scale,
validate_args=validate_args),
len(loc.shape) - 1,
validate_args=validate_args)
super(IndependentNormal, self).__init__(self.base_dist.batch_shape,
self.base_dist.event_shape,
validate_args=validate_args)
def log_prob(self, value):
return self.base_dist.log_prob(value)
@property
def mean(self):
return self.base_dist.mean
@property
def variance(self):
return self.base_dist.variance
def sample(self, sample_shape=torch.Size()):
return self.base_dist.sample(sample_shape)
def rsample(self, sample_shape=torch.Size()):
return self.base_dist.rsample(sample_shape)
def entropy(self):
entropy = self.base_dist.entropy()
return entropy
def get_true_posterior_samples_linear_gaussian_uniform_prior(
observation: torch.Tensor, prior: Independent, num_samples: int = 1000, std=1,
):
observation = utils.torchutils.atleast_2d(observation)
assert observation.ndim == 2, "needs batch dimension in observation"
mean = observation
event_shape = mean.shape[1]
posterior = MultivariateNormal(
loc=mean, covariance_matrix=std * torch.eye(event_shape)
)
# generate samples from ND Gaussian truncated by prior support
num_remaining = num_samples
samples = []
while num_remaining > 0:
candidate_samples = posterior.sample(sample_shape=(num_remaining,))
is_in_prior = torch.isfinite(prior.log_prob(candidate_samples))
# accept if in prior
if is_in_prior.sum():
samples.append(
candidate_samples[is_in_prior,]
)
num_remaining -= is_in_prior.sum().item()
return torch.cat(samples)
def test_independent_expand(self):
for Dist, params in EXAMPLES:
for param in params:
base_dist = Dist(**param)
for reinterpreted_batch_ndims in range(
len(base_dist.batch_shape) + 1):
for s in [
torch.Size(),
torch.Size((2, )),
torch.Size((2, 3))
]:
indep_dist = Independent(base_dist,
reinterpreted_batch_ndims)
expanded_shape = s + indep_dist.batch_shape
expanded = indep_dist.expand(expanded_shape)
expanded_sample = expanded.sample()
expected_shape = expanded_shape + indep_dist.event_shape
self.assertEqual(expanded_sample.shape, expected_shape)
self.assertEqual(
expanded.log_prob(expanded_sample),
indep_dist.log_prob(expanded_sample),
)
self.assertEqual(expanded.event_shape,
indep_dist.event_shape)
self.assertEqual(expanded.batch_shape, expanded_shape)
def trajectory(self, current_state):
'''
Maybe this implementation doesn't utilize GPUs very well, but I have no clue or not.
Final output looks like:
[(s_0, a_0, r_0), ..., (s_L, a_L, r_l)]
'''
output_history = []
while True:
mu, sigma = self.forward(current_state)
distribution = Independent(Normal(mu, sigma), 1)
picked_action = distribution.rsample()
action = picked_action.detach()
#print(action)
new_state, reward = self.env.state_and_reward(
current_state, action
) #Get the reward and the new state that the action in the environment resulted in. None if action caused death. TODO build in environment
#Attempting this
output_history.append(
(current_state, action, reward, distribution.log_prob(action)))
if new_state is None: #essentially, you died or finished your trajectory
break
else:
current_state = new_state
return output_history
def forward( # type: ignore
self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
input: str = "obs",
**kwargs: Any,
) -> Batch:
obs = batch[input]
logits, h = self.actor(obs, state=state, info=batch.info)
assert isinstance(logits, tuple)
dist = Independent(Normal(*logits), 1)
if self._deterministic_eval and not self.training:
act = logits[0]
else:
act = dist.rsample()
log_prob = dist.log_prob(act).unsqueeze(-1)
# apply correction for Tanh squashing when computing logprob from Gaussian
# You can check out the original SAC paper (arXiv 1801.01290): Eq 21.
# in appendix C to get some understanding of this equation.
if self.action_scaling and self.action_space is not None:
action_scale = to_torch_as(
(self.action_space.high - self.action_space.low) / 2.0, act)
else:
action_scale = 1.0 # type: ignore
squashed_action = torch.tanh(act)
log_prob = log_prob - torch.log(action_scale *
(1 - squashed_action.pow(2)) +
self.__eps).sum(-1, keepdim=True)
return Batch(logits=logits,
act=squashed_action,
state=h,
dist=dist,
log_prob=log_prob)
def forward(self, state, eval=False, with_log_prob=False):
x = F.relu(self.fc1(state))
x = F.relu(self.fc2(x))
mu = self.fc3(x)
log_sigma = self.fc4(x)
# clip value of log_sigma, as was done in Haarnoja's implementation of SAC:
# https://github.com/haarnoja/sac.git
log_sigma = torch.clamp(log_sigma, -20.0, 2.0)
sigma = torch.exp(log_sigma)
distribution = Independent(Normal(mu, sigma), 1)
if not eval:
# use rsample() instead of sample(), as sample() does not allow back-propagation through params
u = distribution.rsample()
if with_log_prob:
log_prob = distribution.log_prob(u)
log_prob -= 2.0 * torch.sum(
(np.log(2.0) + 0.5 * np.log(self.ctrl_range) - u -
F.softplus(-2.0 * u)),
dim=1)
else:
log_prob = None
else:
u = mu
log_prob = None
# apply tanh so that the resulting action lies in (-1, 1)^D
a = self.ctrl_range * torch.tanh(u)
return a, log_prob
def forward( # type: ignore
self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
input: str = "obs",
**kwargs: Any,
) -> Batch:
obs = batch[input]
logits, h = self.actor(obs, state=state, info=batch.info)
assert isinstance(logits, tuple)
dist = Independent(Normal(*logits), 1)
if self._deterministic_eval and not self.training:
x = logits[0]
else:
x = dist.rsample()
y = torch.tanh(x)
act = y * self._action_scale + self._action_bias
# __eps is used to avoid log of zero/negative number.
y = self._action_scale * (1 - y.pow(2)) + self.__eps
# Compute logprob from Gaussian, and then apply correction for Tanh squashing.
# You can check out the original SAC paper (arXiv 1801.01290): Eq 21.
# in appendix C to get some understanding of this equation.
log_prob = dist.log_prob(x).unsqueeze(-1)
log_prob = log_prob - torch.log(y).sum(-1, keepdim=True)
return Batch(logits=logits,
act=act,
state=h,
dist=dist,
log_prob=log_prob)
class TanhNormal(Distribution):
"""Copied from Kaixhi"""
def __init__(self, loc, scale):
super().__init__()
self.normal = Independent(Normal(loc, scale), 1)
def sample(self):
return torch.tanh(self.normal.sample())
# samples with re-parametrization trick (differentiable)
def rsample(self):
return torch.tanh(self.normal.rsample())
# Calculates log probability of value using the change-of-variables technique
# (uses log1p = log(1 + x) for extra numerical stability)
def log_prob(self, value):
inv_value = (torch.log1p(value) - torch.log1p(-value)) / 2 # artanh(y)
# log p(f^-1(y)) + log |det(J(f^-1(y)))|
return self.normal.log_prob(inv_value) - torch.log1p(-value.pow(2) +
1e-6).sum(dim=1)
@property
def mean(self):
return torch.tanh(self.normal.mean)
def get_std(self):
return self.normal.stddev
class VAE(nn.Module):
def __init__(self, encoder, decoder, device=None):
super().__init__()
self.encoder = encoder
self.decoder = decoder
if device is None:
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
else:
self.device = device
def forward(self, x=None):
bs = x.size(0)
ls = self.encoder.latent_dims
mu, sigma = self.encoder(x)
self.pz = Independent(Normal(loc=torch.zeros(bs, ls).to(self.device),
scale=torch.ones(bs, ls).to(self.device)),
reinterpreted_batch_ndims=1)
self.qz_x = Independent(Normal(loc=mu, scale=torch.exp(sigma)),
reinterpreted_batch_ndims=1)
self.z = self.qz_x.rsample()
decoded = self.decoder(self.z)
return decoded
def compute_loss(self, x, y, scale_kl=False):
px_z = Independent(ContinuousBernoulli(logits=y),
reinterpreted_batch_ndims=3)
px = px_z.log_prob(x)
kl = self.pz.log_prob(self.z) - self.qz_x.log_prob(self.z)
if scale_kl:
kl = kl * scale_kl
loss = -(px + kl).mean()
return loss, kl.mean().item(), px.mean().item()
def rmse(self, input, target):
return torch.sqrt(F.mse_loss(input, target))
def _loss_vae(self, x):
batch_size = x.size(0)
encoder_output = self.encoder(x)
pz = Independent(Normal(loc=torch.zeros(batch_size, self.latent_dim).to(self.device),
scale=torch.ones(batch_size, self.latent_dim).to(self.device)),
reinterpreted_batch_ndims=1)
qz_x = Independent(Normal(loc=encoder_output[:, :self.latent_dim],
scale=torch.exp(encoder_output[:, self.latent_dim:])),
reinterpreted_batch_ndims=1)
z = qz_x.rsample()
decoder_output = self.decoder(z)
px_z = Independent(Bernoulli(logits=decoder_output),
reinterpreted_batch_ndims=1)
loss = -(px_z.log_prob(x) + pz.log_prob(z) - qz_x.log_prob(z)).mean()
return loss, decoder_output
def forward(self, obs, act=None, deterministic=False):
# Optionally pass in an action to get the log_prob of that action
mu = self.mu_layer(obs)
std = torch.exp(self.log_std_layer)
pi = Independent(Normal(mu, std), 1)
if act is None:
act = pi.mean if deterministic else pi.rsample()
log_prob = pi.log_prob(act)
return pi, act, log_prob
def gaussian_log_likelihood(mu_2d, data_2d, obsrv_std, indices = None): n_data_points = mu_2d.size()[-1] if n_data_points > 0: gaussian = Independent(Normal(loc = mu_2d, scale = obsrv_std.repeat(n_data_points)), 1) log_prob = gaussian.log_prob(data_2d) log_prob = log_prob / n_data_points else: log_prob = torch.zeros([1]).to(get_device(data_2d)).squeeze() return log_prob
def act(self, obs, deterministic=False):
action_mean = self.forward(obs)
normal = Normal(action_mean, torch.exp(self.log_scale))
dist = Independent(normal, 1)
if deterministic:
action = action_mean
else:
action = dist.rsample()
action_logprobs = dist.log_prob(torch.squeeze(action))
return action, action_logprobs
def calc_loglikelihood(inputs: torch.Tensor, outputs: torch.Tensor,
sigma_prior: float):
predicted = outputs.flatten(1)
true_mu = inputs.flatten(1)
base_normal = Normal(
true_mu,
torch.tensor(torch.ones_like(true_mu) * sigma_prior,
dtype=torch.float32,
device=true_mu.device))
mvn = Independent(base_normal, 1)
llh = mvn.log_prob(predicted)
return llh
def compute_loss(self, x, y, scale_kl=False):
px_z = Independent(ContinuousBernoulli(logits=y),
reinterpreted_batch_ndims=3)
px = px_z.log_prob(x)
kl = self.pz.log_prob(self.z) - self.qz_x.log_prob(self.z)
if scale_kl:
kl = kl * scale_kl
loss = -(px + kl).mean()
return loss, kl.mean().item(), px.mean().item()
def loss(self, x):
"""
returns
1. the avergave value of negative ELBO across the minibatch x
2. and the output of the decoder
"""
batch_size = x.size(0)
encoder_output = self.encoder(x)
pz = Independent(Normal(loc=torch.zeros(batch_size,
self.z_dim).to(self.device),
scale=torch.ones(batch_size,
self.z_dim).to(self.device)),
reinterpreted_batch_ndims=1)
qz_x = Independent(Normal(loc=encoder_output[:, :self.z_dim],
scale=torch.exp(
encoder_output[:, self.z_dim:])),
reinterpreted_batch_ndims=1)
z = qz_x.rsample()
decoder_output = self.decoder(z)
px_z = Independent(Bernoulli(logits=decoder_output),
reinterpreted_batch_ndims=1)
loss = -(px_z.log_prob(x) + pz.log_prob(z) - qz_x.log_prob(z)).mean()
return loss, decoder_output
def get_action(self, obs):
obs = torch.tensor(obs, dtype=torch.float).to(self.device)
with torch.no_grad():
mu, sigma = self.pi(obs)
act_distribution = Independent(Normal(mu, sigma), 1)
action = act_distribution.sample()
log_prob = act_distribution.log_prob(action)
val = self.V(obs)
action = action.cpu().numpy()
log_prob = log_prob.cpu().numpy()
val = val.cpu().numpy()
return action, log_prob, val
def mdn_loss_fn(self, mu, sigma, y, epsilon=1e-9):
# Non-vectorised version
#result = torch.zeros(y.shape[0], self.n_gaussians).to(self.device)
# for idx in range(self.n_gaussians):
# gaussian = Independent(Normal(loc=mu[:, :, idx], scale=sigma), 1)
# result_per_gaussian = gaussian.log_prob(y)
# result[:, idx] = result_per_gaussian + self.pi.log()
# return -torch.mean(torch.logsumexp(result, dim=1))
gaussian = Independent(
Normal(loc=mu,
scale=sigma.reshape(-1, self.n_outputs,
1).repeat(1, 1, mu.shape[2])), 0)
result = gaussian.log_prob(
y.reshape([-1, mu.shape[1], 1]).repeat(1, 1, self.n_gaussians))
result = torch.sum(result, dim=1) + self.pi.log()
return -torch.mean(torch.logsumexp(result, dim=1))
def forward(self, obs, deterministic=False):
mu = self.mu_layer(obs)
log_std = self.log_std_layer(obs)
std = torch.clamp(log_std, LOG_STD_MIN, LOG_STD_MAX).exp()
# Pre-squash distribution and sample
pi_distribution = Independent(Normal(mu, std), 1)
act = mu if deterministic else pi_distribution.rsample()
log_prob = pi_distribution.log_prob(act)
squashed_action = torch.tanh(act)
log_prob = log_prob - torch.log((1 - squashed_action.pow(2)) +
self.__eps).sum(axis=-1)
return squashed_action, log_prob
def loss_vae_normal(x, encoder, decoder):
batch_size = x.size(0)
encoder_output = encoder(x)
d = encoder_output.shape[1] // 2
pz_loc = F.sigmoid(torch.zeros(batch_size, d).to(device))
pz_scale = torch.ones(batch_size, d).to(device)
pz = Independent(Normal(loc=pz_loc, scale=pz_scale),
reinterpreted_batch_ndims=1)
qz_x_loc = encoder_output[:, :d]
qz_x_log_scale = encoder_output[:, d:]
qz_x = Independent(Normal(loc=qz_x_loc, scale=qz_x_log_scale**2),
reinterpreted_batch_ndims=1)
z = qz_x.rsample()
decoder_output = decoder(z)
optimal_sigma_observed = ((x - decoder_output)**2).mean(
[0, 1, 2, 3], keepdim=True).sqrt()
px_z = Independent(Normal(loc=decoder_output,
scale=optimal_sigma_observed),
reinterpreted_batch_ndims=3)
elbo = (px_z.log_prob(x) - kl_divergence(qz_x, pz)).mean()
return -elbo, decoder_output
def test_independent_shape(self):
for Dist, params in EXAMPLES:
for param in params:
base_dist = Dist(**param)
x = base_dist.sample()
base_log_prob_shape = base_dist.log_prob(x).shape
for reinterpreted_batch_ndims in range(
len(base_dist.batch_shape) + 1):
indep_dist = Independent(base_dist,
reinterpreted_batch_ndims)
indep_log_prob_shape = base_log_prob_shape[:len(
base_log_prob_shape) - reinterpreted_batch_ndims]
self.assertEqual(
indep_dist.log_prob(x).shape, indep_log_prob_shape)
self.assertEqual(indep_dist.sample().shape,
base_dist.sample().shape)
self.assertEqual(indep_dist.has_rsample,
base_dist.has_rsample)
if indep_dist.has_rsample:
self.assertEqual(indep_dist.sample().shape,
base_dist.sample().shape)
try:
self.assertEqual(
indep_dist.enumerate_support().shape,
base_dist.enumerate_support().shape,
)
self.assertEqual(indep_dist.mean.shape,
base_dist.mean.shape)
except NotImplementedError:
pass
try:
self.assertEqual(indep_dist.variance.shape,
base_dist.variance.shape)
except NotImplementedError:
pass
try:
self.assertEqual(indep_dist.entropy().shape,
indep_log_prob_shape)
except NotImplementedError:
pass
def reinforce_loss(policy,
episodes,
init_std=1.0,
min_std=1e-6,
output_size=2
):
output = policy(episodes.observations.view((-1, *episodes.observation_shape)))
min_log_std = math.log(min_std)
sigma = nn.Parameter(torch.Tensor(output_size))
sigma.data.fill_(math.log(init_std))
scale = torch.exp(torch.clamp(sigma, min=min_log_std))
pi = Independent(Normal(loc=output, scale=scale), 1)
log_probs = pi.log_prob(episodes.actions.view((-1, *episodes.action_shape)))
log_probs = log_probs.view(len(episodes), episodes.batch_size)
losses = -weighted_mean(log_probs * episodes.advantages,
lengths=episodes.lengths)
return losses.mean()
class IndependentRescaledBeta(Distribution):
arg_constraints = {
'concentration1': constraints.positive,
'concentration0': constraints.positive
}
support = constraints.interval(-1., 1.)
has_rsample = True
def __init__(self, concentration1, concentration0, validate_args=None):
self.base_dist = Independent(RescaledBeta(concentration1,
concentration0,
validate_args),
len(concentration1.shape) - 1,
validate_args=validate_args)
super(IndependentRescaledBeta,
self).__init__(self.base_dist.batch_shape,
self.base_dist.event_shape,
validate_args=validate_args)
def log_prob(self, value):
return self.base_dist.log_prob(value)
@property
def mean(self):
return self.base_dist.mean
@property
def variance(self):
return self.base_dist.variance
def sample(self, sample_shape=torch.Size()):
return self.base_dist.sample(sample_shape)
def rsample(self, sample_shape=torch.Size()):
return self.base_dist.rsample(sample_shape)
def entropy(self):
entropy = self.base_dist.entropy()
return entropy
def loss_function(x_hat, x, q_z, z, epoch):
if args.loss=='mixture':
BCE = torch.mean(-log_mix_dep_Logistic_256(x, x_hat, average=True, n_comps=10))
if args.loss=='CE':
x_hat = x_hat.view(-1, 3, 256, 64, 64)
x_hat = x_hat.permute(0, 1, 3, 4, 2)
x_hat = x_hat.contiguous()
x_hat = x_hat.view(-1, 256)
#x_hat = torch.round(256 * x_hat.view(-1, 256))
target = Variable(x.data.view(-1) * 255).long()
BCE = loss(x_hat, target)
#x = x.view(-1, x_hat.size(1))
#tensor = torch.ones(1)
#p_x_dist = Beta(tensor.new_full((z.size(0), z_dim), 0.5).to(device), tensor.new_full((z.size(0), z_dim), 0.5).to(device))
z_sqrt = int(np.sqrt(z_dim))
if arch == 'resnet' or arch == 'convlin':
p_x_dist = Independent(distri(torch.zeros(z.size(0), z_dim).to(device), torch.ones(z.size(0), z_dim).to(device)), 1)
else:
p_x_dist = Independent(distri(torch.zeros(z.size(0), 1, z_sqrt, z_sqrt).to(device), torch.ones(z.size(0), 1, z_sqrt, z_sqrt).to(device)), 1)
one_third = round(args.epochs/3)
if beta_final>=1:
if epoch<=one_third:
beta = (beta_final*epoch)/one_third
else:
beta = beta_final
else:
beta = 1
#BCE = torch.sum(-p_x.log_prob(x.view(x.size(0), x_dim**2)))
KLD = torch.mean(q_z.log_prob(z) - p_x_dist.log_prob(z))
print(BCE, KLD, beta)
return (BCE + beta*KLD), BCE, KLD
def __call__(self, x, out_keys=['action'], info={}, **kwargs):
# Output dictionary
out_policy = {}
# Forward pass of feature networks to obtain features
if self.recurrent:
out_network = self.network(x=x,
hidden_states=self.rnn_states,
mask=info.get('mask', None))
features = out_network['output']
# Update the tracking of current RNN hidden states
self.rnn_states = out_network['hidden_states']
else:
features = self.network(x)
# Forward pass through mean head to obtain mean values for Gaussian distribution
mean = self.network.mean_head(features)
# Obtain logvar based on the options
if isinstance(self.network.logvar_head,
nn.Linear): # linear layer, then do forward pass
logvar = self.network.logvar_head(features)
else: # either Tensor or nn.Parameter
logvar = self.network.logvar_head
# Expand as same shape as mean
logvar = logvar.expand_as(mean)
# Forward pass of value head to obtain value function if required
if 'state_value' in out_keys:
out_policy['state_value'] = self.network.value_head(
features).squeeze(-1) # squeeze final single dim
# Get std from logvar
if self.std_style == 'exp':
std = torch.exp(0.5 * logvar)
elif self.std_style == 'softplus':
std = F.softplus(logvar)
# Lower bound threshould for std
min_std = torch.full(std.size(),
self.min_std).type_as(std).to(self.device)
std = torch.max(std, min_std)
# Create independent Gaussian distributions i.e. Diagonal Gaussian
action_dist = Independent(Normal(loc=mean, scale=std), 1)
# Sample action from the distribution (no gradient)
# Do not use `rsample()`, it leads to zero gradient of mean head !
action = action_dist.sample()
out_policy['action'] = action
# Calculate log-probability of the sampled action
if 'action_logprob' in out_keys:
out_policy['action_logprob'] = action_dist.log_prob(action)
# Calculate policy entropy conditioned on state
if 'entropy' in out_keys:
out_policy['entropy'] = action_dist.entropy()
# Calculate policy perplexity i.e. exp(entropy)
if 'perplexity' in out_keys:
out_policy['perplexity'] = action_dist.perplexity()
# sanity check for NaN
if torch.any(torch.isnan(action)):
while True:
msg = 'NaN ! A workaround is to learn state-independent std or use tanh rather than relu'
msg2 = f'check: \n\t mean: {mean}, logvar: {logvar}'
print(msg + msg2)
# Constraint action in valid range
out_policy['action'] = self.constraint_action(action)
return out_policy
def normal_log_density(means, stds, actions):
dist = Independent(Normal(means, stds), 1)
return dist.log_prob(actions)
b, m, train_m, test_m = make_batch_mask(batch, param)
input_tuple = (b, m, train_m, test_m)
#tec = time.time()
#print('Batch got in %.2f sec' % (tec - tic))
optimizer.zero_grad()
output = model.forward(input_tuple)
masked_output = output[test_m.bool()].reshape(
param['batch_size'], (param['total_points'] - param['obs_points']))
target = b[:, :, 1][test_m.bool()].reshape(
param['batch_size'], (param['total_points'] - param['obs_points']))
log_likelihood = torch.tensor(0.0)
for i in range(masked_output.shape[0]):
gaussian = Independent(Normal(masked_output[i], param['sigma']), 1)
ll = gaussian.log_prob(target[i]) / masked_output.shape[1]
log_likelihood += ll
log_likelihood /= masked_output.shape[0]
loss = -log_likelihood
mse_loss = mse(masked_output, target)
#tac = time.time()
#print('Forward finished in %.2f sec' % (tac - tec))
loss.backward()
#tuc = time.time()
#print('Backward fininshed in %.2f sec' % (tuc - tac))
optimizer.step()
toc = time.time()
for k in range(param['figure_per_batch']):
plt.clf()
