args.rnn_depth, args.rnn_dropout,
observation_dim)
self.rnn_init_hidden = nn.Parameter(torch.zeros(1, args.rnn_hidden_size))
self.sigma2 = nn.Parameter(sigma2 * torch.ones(observation_dim))
self.transition_bias = transition_bias
def save(self, filepath):
Save the model to a file.
After Change
self.rnn_init_hidden = nn.Parameter(
torch.zeros(1, args.rnn_hidden_size).to(self.device))
sigma2 = .1 if args.sigma2 is None else args.sigma2
self.sigma2 = nn.Parameter(
sigma2 * torch.ones(observation_dim).to(self.device))
self.transition_bias = transition_bias
def save(self, filepath):
Save the model to a file.
