def __init__(self,
n_actions,
n_input_channels=4,
activation=F.relu,
bias=0.1,
reward_boundaries=None,
reward_channel_scale=1.):
self.n_actions = n_actions
self.n_input_channels = n_input_channels
self.activation = activation
self.boundaries = torch.from_numpy(
np.array(reward_boundaries)) * reward_channel_scale - 1e-8
super().__init__()
self.conv_layers = nn.ModuleList([
nn.Conv2d(n_input_channels, 32, 8, stride=4),
nn.Conv2d(32, 64, 4, stride=2),
nn.Conv2d(64, 64, 3, stride=1),
])
# Modified from 3136 -> 1024
self.a_streams = nn.ModuleList([
MLP(1024, n_actions, [512])
for _ in range(len(self.boundaries) + 1)
])
self.v_streams = nn.ModuleList(
[MLP(1024, 1, [512]) for _ in range(len(self.boundaries) + 1)])
self.conv_layers.apply(init_chainer_default) # MLP already applies
self.conv_layers.apply(constant_bias_initializer(bias=bias))
def __init__(self,
n_actions,
n_input_channels=4,
activation=F.relu,
bias=0.1):
self.n_actions = n_actions
self.n_input_channels = n_input_channels
self.activation = activation
super().__init__()
self.conv_layers = nn.ModuleList([
nn.Conv2d(n_input_channels, 32, 8, stride=4),
nn.Conv2d(32, 64, 4, stride=2),
nn.Conv2d(64, 64, 3, stride=1),
])
# Modified from 3136 -> 1024
self.a_stream = MLP(1024, n_actions, [512])
self.v_stream = MLP(1024, 1, [512])
self.conv_layers.apply(init_chainer_default) # MLP already applies
self.conv_layers.apply(constant_bias_initializer(bias=bias))
def __init__(
self,
n_actions,
n_atoms,
v_min,
v_max,
n_input_channels=4,
activation=torch.relu,
bias=0.1,
):
assert n_atoms >= 2
assert v_min < v_max
self.n_actions = n_actions
self.n_input_channels = n_input_channels
self.activation = activation
self.n_atoms = n_atoms
super().__init__()
self.z_values = torch.linspace(v_min,
v_max,
n_atoms,
dtype=torch.float32)
self.conv_layers = nn.ModuleList([
nn.Conv2d(n_input_channels, 32, 8, stride=4),
nn.Conv2d(32, 64, 4, stride=2),
nn.Conv2d(64, 64, 3, stride=1),
])
# ここだけ変える必要があった
# self.main_stream = nn.Linear(3136, 1024)
self.main_stream = nn.Linear(1024, 1024)
self.a_stream = nn.Linear(512, n_actions * n_atoms)
self.v_stream = nn.Linear(512, n_atoms)
self.apply(init_chainer_default)
self.conv_layers.apply(constant_bias_initializer(bias=bias))