def _unsquash(self, values):
normed_values = (values - self.low) / (self.high - self.low) * 2.0 - \
1.0
# Stabilize input to atanh.
save_normed_values = torch.clamp(normed_values, -1.0 + SMALL_NUMBER,
1.0 - SMALL_NUMBER)
unsquashed = atanh(save_normed_values)
return unsquashed
def bc_log(model, obs, actions):
z = atanh(actions)
logits = model.get_policy_output(obs)
mean, log_std = torch.chunk(logits, 2, dim=-1)
# Mean Clamping for Stability
mean = torch.clamp(mean, MEAN_MIN, MEAN_MAX)
log_std = torch.clamp(log_std, MIN_LOG_NN_OUTPUT,
MAX_LOG_NN_OUTPUT)
std = torch.exp(log_std)
normal_dist = torch.distributions.Normal(mean, std)
return torch.sum(normal_dist.log_prob(z) -
torch.log(1 - actions * actions + SMALL_NUMBER),
dim=-1)
def bc_log(model, obs, actions):
# Stabilize input to atanh.
normed_actions = \
(actions - action_dist_t.low) / \
(action_dist_t.high - action_dist_t.low) * 2.0 - 1.0
save_normed_actions = torch.clamp(
normed_actions, -1.0 + SMALL_NUMBER, 1.0 - SMALL_NUMBER)
z = atanh(save_normed_actions)
logits = model.get_policy_output(obs)
mean, log_std = torch.chunk(logits, 2, dim=-1)
# Mean Clamping for Stability
mean = torch.clamp(mean, MEAN_MIN, MEAN_MAX)
log_std = torch.clamp(log_std, MIN_LOG_NN_OUTPUT,
MAX_LOG_NN_OUTPUT)
std = torch.exp(log_std)
normal_dist = torch.distributions.Normal(mean, std)
return torch.sum(
normal_dist.log_prob(z) -
torch.log(1 - actions * actions + SMALL_NUMBER),
dim=-1)
def _unsquash(self, values):
return atanh((values - self.low) / (self.high - self.low) * 2.0 - 1.0)