def _init_params_nets(self):
self.arguments_networks = {}
self.arguments_dict = {}
for a in self.action_dict:
action = self.all_actions[self.action_dict[a]]
args = action.args
for arg in args:
self.arguments_dict[
arg.name] = arg.id # store 'name':id pairs for future use
if debug: print('\narg.name: ', arg.name)
size = self.all_arguments[arg.id].sizes
if debug: print('size: ', size)
if len(size) == 1:
if debug:
print("Init CategoricalNet for " + arg.name +
' argument')
self.arguments_networks[arg.name] = net.CategoricalNet(
self.net.n_features, size[0])
else:
if debug:
print("Init SpatialNet for " + arg.name + ' argument')
self.arguments_networks[arg.name] = net.SpatialNet(
self.net.n_features, size)
return
def __init__(self, action_space, env, n_layers, linear_size, in_channels,
n_channels, **non_spatial_HPs):
"""
Parameters
----------
action_space: int
Number of (discrete) possible actions to take
map_size: int
If input is (batch_dim, n_channels, linear_size, linear_size),
then map_size = linear_size - 2
env: int
Instance of the environment
**net_args: dict (optional)
Dictionary of {'key':value} pairs valid for OheNet
"""
super(SpatialActor, self).__init__()
# Environment-related attributes
self.action_dict = {0: _NO_OP, 1: _SELECT_ARMY, 2: _MOVE_SCREEN}
self.screen_res = env.observation_spec()[0]['feature_screen'][1:]
self.all_actions = env.action_spec()[0][1]
self.all_arguments = env.action_spec()[0][0]
# Useful HyperParameters as attributes
self.n_channels = n_channels
# Networks
self.spatial_features_net = net.SpatialFeatures(
n_layers, linear_size, in_channels, n_channels)
self.nonspatial_features_net = net.NonSpatialFeatures(
linear_size, n_channels, **non_spatial_HPs)
self.linear = nn.Linear(n_channels, action_space)
self._init_params_nets()
def __init__(self, n_layers, linear_size, in_channels, n_channels,
**non_spatial_HPs):
"""
Parameters
----------
map_size: int
If input is (batch_dim, n_channels, linear_size, linear_size),
then map_size = linear_size - 2
**net_args: dict (optional)
Dictionary of {'key':value} pairs valid for OheNet
"""
super(SpatialBasicCritic, self).__init__()
self.net = nn.Sequential(
net.SpatialFeatures(n_layers, linear_size, in_channels,
n_channels),
net.NonSpatialFeatures(linear_size, n_channels, **non_spatial_HPs),
nn.Linear(n_channels, 1))
def __init__(self, map_size, **net_args):
"""
Parameters
----------
map_size: int
If input is (batch_dim, n_channels, linear_size, linear_size),
then map_size = linear_size - 2
**net_args: dict (optional)
Dictionary of {'key':value} pairs valid for OheNet
"""
super(OheBasicCritic, self).__init__()
self.net = net.OheNet(map_size, **net_args)
self.linear = nn.Linear(self.net.n_features, 1)
def __init__(self, action_space, map_size, **net_args):
"""
Parameters
----------
action_space: int
Number of (discrete) possible actions to take
map_size: int
If input is (batch_dim, n_channels, linear_size, linear_size),
then map_size = linear_size - 2
**net_args: dict (optional)
Dictionary of {'key':value} pairs valid for OheNet
"""
super(OheActor, self).__init__()
self.action_dict = {0: _NO_OP, 1: _SELECT_ARMY, 2: _MOVE_SCREEN}
self.net = net.OheNet(map_size, **net_args)
self.linear = nn.Linear(self.net.n_features, action_space)