def __init__(self, input_shape, n_actions):
super(RainbowDQN, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(input_shape[0], 32, kernel_size=8, stride=4),
nn.ReLU(),
nn.Conv2d(32, 64, kernel_size=4, stride=2),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, stride=1),
nn.ReLU()
)
conv_out_size = self._get_conv_out(input_shape)
self.fc_val = nn.Sequential(
dqn_model.NoisyLinear(conv_out_size, 512),
nn.ReLU(),
dqn_model.NoisyLinear(512, N_ATOMS)
)
self.fc_adv = nn.Sequential(
dqn_model.NoisyLinear(conv_out_size, 512),
nn.ReLU(),
dqn_model.NoisyLinear(512, n_actions * N_ATOMS)
)
self.register_buffer("supports", torch.arange(Vmin, Vmax, DELTA_Z))
self.softmax = nn.Softmax(dim=1)
def __init__(self, obs_size, act_size):
super(DDPGActor, self).__init__()
self.net = nn.Sequential(dqn_model.NoisyLinear(obs_size,
400), nn.ReLU(),
dqn_model.NoisyLinear(400, 300), nn.ReLU(),
dqn_model.NoisyLinear(300, act_size),
nn.Tanh())
def __init__(self, input_shape, n_actions):
super(RainbowDQN, self).__init__()
self.fc_val = nn.Sequential(
dqn_model.NoisyLinear(input_shape, NUMBER_NEURONS), nn.ReLU(),
dqn_model.NoisyLinear(NUMBER_NEURONS, N_ATOMS))
self.fc_adv = nn.Sequential(
dqn_model.NoisyLinear(input_shape, NUMBER_NEURONS), nn.ReLU(),
dqn_model.NoisyLinear(NUMBER_NEURONS, n_actions * N_ATOMS))
self.register_buffer("supports",
torch.arange(Vmin, Vmax + DELTA_Z, DELTA_Z))
self.softmax = nn.Softmax(dim=1)
def __init__(self, input_shape, n_actions):
super(NoisyDQN, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(input_shape[0], 32, kernel_size=8, stride=4), nn.ReLU(),
nn.Conv2d(32, 64, kernel_size=4, stride=2), nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, stride=1), nn.ReLU())
conv_out_size = self._get_conv_out(input_shape)
self.noisy_layers = [
dqn_model.NoisyLinear(conv_out_size, 512),
dqn_model.NoisyLinear(512, n_actions)
]
self.fc = nn.Sequential(self.noisy_layers[0], nn.ReLU(),
self.noisy_layers[1])
