def test_mlp(self):
"""
test getting sequential MLP
"""
sizes = [2, 3, 3, 2]
mlp_nn = mlp(sizes)
mlp_nn_sac = mlp(sizes, sac=True)
assert len(mlp_nn) == 2 * (len(sizes) - 1)
assert all(isinstance(mlp_nn[i], nn.Linear) for i in range(0, 5, 2))
assert len(mlp_nn_sac) == 2 * (len(sizes) - 2)
assert all(isinstance(mlp_nn_sac[i], nn.Linear) for i in range(0, 4, 2))
inp = torch.randn((2,))
assert mlp_nn(inp).shape == (2,)
assert mlp_nn_sac(inp).shape == (3,)
def __init__(self, *args, **kwargs):
super(CnnValue, self).__init__(*args, **kwargs)
self.conv, self.output_size = cnn(
(self.state_dim, 16, 32), activation=self.activation
)
self.model = mlp([self.output_size, *self.fc_layers, self.action_dim])
def __init__(self,
framestack: int,
action_dim: int,
hidden: Tuple = (32, 32),
discrete: bool = True,
*args,
**kwargs):
super(CNNPolicy, self).__init__(framestack, action_dim, hidden,
discrete, **kwargs)
channels = (framestack, 16, 32)
self.conv, output_size = cnn(channels)
self.fc = mlp([output_size] + list(hidden) + [action_dim],
sac=self.sac)
def __init__(self,
state_dim: int,
action_dim: int,
hidden: Tuple = (32, 32),
discrete: bool = True,
*args,
**kwargs):
super(MlpPolicy, self).__init__(state_dim, action_dim, hidden,
discrete, **kwargs)
self.activation = kwargs[
"activation"] if "activation" in kwargs else "relu"
self.model = mlp(
[state_dim] + list(hidden) + [action_dim],
activation=self.activation,
sac=self.sac,
)
def __init__(self, *args, **kwargs):
super(CnnDuelingValue, self).__init__(*args, **kwargs)
self.advantage = mlp(
[self.output_size, *self.fc_layers, self.action_dim])
self.value = mlp([self.output_size, *self.fc_layers, 1])
def __init__(self, *args, **kwargs):
super(MlpDuelingValue, self).__init__(*args, **kwargs)
self.feature = mlp([self.state_dim, *self.fc_layers[:-1]])
self.advantage = mlp([self.fc_layers[-1], self.action_dim])
self.value = mlp([self.fc_layers[-1], 1])
