def __init__(self,
num_inputs,
recurrent=False,
recurrent_type="GRU",
hidden_size=64):
super(MLPBase, self).__init__(recurrent, num_inputs, recurrent_type,
hidden_size)
if recurrent:
num_inputs = hidden_size
init_ = lambda m: init(m, nn.init.orthogonal_, lambda x: nn.init.
constant_(x, 0), np.sqrt(2))
self.actor = nn.Sequential(init_(nn.Linear(num_inputs, hidden_size)),
nn.Tanh(),
init_(nn.Linear(hidden_size, hidden_size)),
nn.Tanh())
self.critic = nn.Sequential(init_(nn.Linear(num_inputs, hidden_size)),
nn.Tanh(),
init_(nn.Linear(hidden_size, hidden_size)),
nn.Tanh())
self.critic_linear = init_(nn.Linear(hidden_size, 1))
self.train()
def __init__(self, num_inputs, recurrent=False, hidden_size=512):
super(CNNBase, self).__init__(recurrent, hidden_size, hidden_size)
init_ = lambda m: init(m, nn.init.orthogonal_, lambda x: nn.init.
constant_(x, 0), nn.init.calculate_gain('relu'))
self.main = nn.Sequential(
init_(nn.Conv2d(num_inputs, 32, 8, stride=4)), nn.ReLU(),
init_(nn.Conv2d(32, 64, 4, stride=2)), nn.ReLU(),
init_(nn.Conv2d(64, 32, 3, stride=1)), nn.ReLU(), Flatten(),
init_(nn.Linear(32 * 7 * 7, hidden_size)), nn.ReLU())
init_ = lambda m: init(m, nn.init.orthogonal_, lambda x: nn.init.
constant_(x, 0))
self.critic_linear = init_(nn.Linear(hidden_size, 1))
self.train()
def __init__(self, num_inputs, num_outputs):
super(Categorical, self).__init__()
init_ = lambda m: init(m,
nn.init.orthogonal_,
lambda x: nn.init.constant_(x, 0),
gain=0.01)
self.linear = init_(nn.Linear(num_inputs, num_outputs))
def init_weight(self, layer):
init_ = lambda m: init(m, nn.init.orthogonal_, lambda x: nn.init.
constant_(x, 0), nn.init.calculate_gain('relu'))
if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear):
return init_(layer)
elif isinstance(layer, nn.BatchNorm2d):
layer.weight.data.fill_(1)
if hasattr(layer, 'bias'):
layer.bias.data.zero_()
return layer
def __init__(self, num_inputs, num_outputs):
super(DiagGaussian, self).__init__()
init_ = lambda m: init(m, nn.init.orthogonal_, lambda x: nn.init.
constant_(x, 0))
self.fc_mean = init_(nn.Linear(num_inputs, num_outputs))
desired_init_log_std = -0.693471 #exp(..) ~= 0.5
self.logstd = AddBias(
desired_init_log_std *
torch.ones(num_outputs)) #so no state-dependent sigma
def init_(m):
return init(m, nn.init.orthogonal_,
lambda x: nn.init.constant_(x, 0))