def __init__(self, obs_dim, act_dim, conv_layer_sizes, hidden_sizes,
activation):
'''
A Convolutional Neural Net for the Actor network for Continuous outputs
Network Architecture: (input) -> CNN -> MLP -> (output)
Assume input is in the shape: (3, 128, 128)
Args:
obs_dim (tuple): observation dimension of the environment in the form of (C, H, W)
act_dim (int): action dimension of the environment
conv_layer_sizes (list): list of 3-tuples consisting of (output_channel, kernel_size, stride)
that describes the cnn architecture
hidden_sizes (list): list of number of neurons in each layer of MLP after output from CNN
activation (nn.modules.activation): Activation function for each layer of MLP
act_limit (float): the greatest magnitude possible for the action in the environment
'''
super().__init__()
log_std = -0.5 * np.ones(act_dim, dtype=np.float32)
self.log_std = torch.nn.Parameter(torch.as_tensor(log_std))
self.mu_cnn = cnn(obs_dim[0],
conv_layer_sizes,
activation,
batchnorm=True)
self.start_dim = self.calc_shape(obs_dim, self.mu_cnn)
mlp_sizes = [self.start_dim] + list(hidden_sizes) + [act_dim]
self.mu_mlp = mlp(mlp_sizes, activation, output_activation=nn.Tanh)
# initialise actor network final layer weights to be 1/100 of other weights
self.mu_mlp[
-2].weight.data /= 100 # last layer is Identity, so we tweak second last layer weights
def __init__(self, obs_dim, act_dim, conv_layer_sizes, hidden_sizes,
activation, act_limit):
'''
A Convolutional Neural Net for the Actor network
Network Architecture: (input) -> CNN -> MLP -> (output)
Assume input is in the shape: (3, 128, 128)
Args:
obs_dim (tuple): observation dimension of the environment in the form of (C, H, W)
act_dim (int): action dimension of the environment
conv_layer_sizes (list): list of 3-tuples consisting of (output_channel, kernel_size, stride)
that describes the cnn architecture
hidden_sizes (list): list of number of neurons in each layer of MLP after output from CNN
activation (nn.modules.activation): Activation function for each layer of MLP
act_limit (float): the greatest magnitude possible for the action in the environment
'''
super().__init__()
self.pi_cnn = cnn(obs_dim[0],
conv_layer_sizes,
activation,
batchnorm=True)
self.start_dim = self.calc_shape(obs_dim, self.pi_cnn)
mlp_sizes = [self.start_dim] + list(hidden_sizes) + [act_dim]
self.pi_mlp = mlp(mlp_sizes, activation, output_activation=nn.Tanh)
self.act_limit = act_limit
def calc_shape(self, obs_dim, cnn):
'''
Function to determine the shape of the data after the conv layers
to determine how many neurons for the MLP.
'''
C, H, W = obs_dim
dummy_input = torch.randn(1, C, H, W)
with torch.no_grad():
cnn_out = cnn(dummy_input)
shape = cnn_out.view(-1, ).shape[0]
return shape
def __init__(self, obs_dim, conv_layer_sizes, hidden_sizes, activation):
'''
A Convolutional Neural Net for the Critic network
Args:
obs_dim (tuple): observation dimension of the environment in the form of (C, H, W)
act_dim (int): action dimension of the environment
conv_layer_sizes (list): list of 3-tuples consisting of (output_channel, kernel_size, stride)
that describes the cnn architecture
hidden_sizes (list): list of number of neurons in each layer of MLP
activation (nn.modules.activation): Activation function for each layer of MLP
'''
super().__init__()
self.v_cnn = cnn(obs_dim[0], conv_layer_sizes, activation, batchnorm=True)
self.start_dim = self.calc_shape(obs_dim, self.v_cnn)
self.v_mlp = mlp([self.start_dim] + list(hidden_sizes) + [1], activation)
def __init__(self, obs_dim, act_dim, conv_layer_sizes, hidden_sizes, activation):
'''
A Convolutional Neural Net for the Actor network for discrete outputs
Network Architecture: (input) -> CNN -> MLP -> (output)
Assume input is in the shape: (3, 128, 128)
Args:
obs_dim (tuple): observation dimension of the environment in the form of (C, H, W)
act_dim (int): action dimension of the environment
conv_layer_sizes (list): list of 3-tuples consisting of (output_channel, kernel_size, stride)
that describes the cnn architecture
hidden_sizes (list): list of number of neurons in each layer of MLP after output from CNN
activation (nn.modules.activation): Activation function for each layer of MLP
'''
super().__init__()
self.logits_cnn = cnn(obs_dim[0], conv_layer_sizes, activation, batchnorm=True)
self.start_dim = self.calc_shape(obs_dim, self.logits_cnn)
mlp_sizes = [self.start_dim] + list(hidden_sizes) + [act_dim]
self.logits_mlp = mlp(mlp_sizes, activation, output_activation=nn.Tanh)
# initialise actor network final layer weights to be 1/100 of other weights
self.logits_mlp[-2].weight.data /= 100 # last layer is Identity, so we tweak second last layer weights
