def __init__(
self,
obs_dim: int,
hidden_sizes: Union[List[int], Tuple[int]],
activation: torch.nn.Module,
):
super().__init__()
self.f_net = mlp(
[obs_dim, hidden_sizes[0]],
activation,
)
self.v_net = mlp(list(hidden_sizes[1:]) + [1], activation)
self.apply(init_weights)
def __init__(
self,
obs_dim: int,
act_space: Box,
hidden_sizes: Union[List[int], Tuple[int]],
activation: torch.nn.Module,
history_len: int,
feature_dim: int = 25,
):
super().__init__()
act_dim = act_space.shape[0]
self.act_high = torch.as_tensor(act_space.high)
self.act_low = torch.as_tensor(act_space.low)
self.net = mlp(
[obs_dim + feature_dim] + list(hidden_sizes),
activation,
activation,
)
self.mu_layer = nn.Linear(hidden_sizes[-1], act_dim)
self.lidar_features = nn.Sequential(
nn.Conv1d(history_len, 1, 4, 2, 2, padding_mode="circular"),
nn.Conv1d(1, 1, 4, 2, 2, padding_mode="circular"),
nn.AdaptiveAvgPool1d(feature_dim),
)
self.log_std = nn.Parameter(-0.5 * torch.ones(act_dim)).unsqueeze(0)
self.history_len = history_len
self.apply(init_weights)
def __init__(
self,
obs_dim: int,
act_space: Discrete,
hidden_sizes: Union[List[int], Tuple[int]] = [256, 256],
activation: nn.Module = nn.ReLU,
):
super().__init__()
self.deviation_net = mlp(
[obs_dim] + list(hidden_sizes) + [act_space.n],
activation,
)
self.apply(init_weights)
def __init__(
self,
obs_dim: int,
hidden_sizes: Union[List[int], Tuple[int]],
activation: torch.nn.Module,
nagents: int,
):
super().__init__()
self.v_net = mlp(
[obs_dim * nagents] + list(hidden_sizes) + [1],
activation,
)
self.nagents = nagents
self.apply(init_weights)
def __init__(
self,
obs_dim: int,
hidden_sizes: Union[List[int], Tuple[int]],
activation: torch.nn.Module,
history_len: int,
feature_dim: int = 25,
):
super().__init__()
self.feature_net = mlp(
[obs_dim + feature_dim] + [hidden_sizes[0]],
activation,
)
self.lidar_features = nn.Sequential(
nn.Conv1d(history_len, 1, 4, 2, 2, padding_mode="circular"),
nn.Conv1d(1, 1, 4, 2, 2, padding_mode="circular"),
nn.AdaptiveAvgPool1d(feature_dim),
)
self.v_net = mlp(
list(hidden_sizes) + [1],
activation,
)
self.history_len = history_len
self.apply(init_weights)
def __init__(
self,
obs_dim: int,
act_space: Box,
hidden_sizes: Union[List[int], Tuple[int]] = [256, 256],
activation: torch.nn.Module = torch.nn.ReLU,
):
super().__init__()
act_dim = act_space.shape[0]
self.act_high = torch.as_tensor(act_space.high)
self.act_low = torch.as_tensor(act_space.low)
self.net = mlp(
[obs_dim] + list(hidden_sizes),
activation,
)
self.mu_layer = nn.Linear(hidden_sizes[-1], act_dim)
self.log_std = nn.Parameter(-0.5 * torch.ones(act_dim))
self.apply(init_weights)