def _init(self, args):
params = args.params
assert isinstance(
params["num_action"],
int), "num_action has to be a number. action = " + str(
params["num_action"])
self.params = params
self.net = MiniRTSNet(args)
last_num_channel = self.net.num_channels[-1]
if self.params.get("model_no_spatial", False):
self.num_unit = params["num_unit_type"]
linear_in_dim = last_num_channel
else:
linear_in_dim = last_num_channel * 25
self.linear_policy = nn.Linear(linear_in_dim, params["num_action"])
self.linear_value = nn.Linear(linear_in_dim, 1)
self.relu = nn.LeakyReLU(0.1)
self.Wt = nn.Linear(linear_in_dim + params["num_action"],
linear_in_dim)
self.Wt2 = nn.Linear(linear_in_dim, linear_in_dim)
self.Wt3 = nn.Linear(linear_in_dim, linear_in_dim)
self.softmax = nn.Softmax()
def _init(self, args):
params = args.params
assert isinstance(
params["num_action"],
int), "num_action has to be a number. action = " + str(
params["num_action"])
self.params = params
self.net = MiniRTSNet(args, output1d=False)
self.na = params["num_action"]
self.num_unit = params["num_unit_type"]
self.num_planes = params["num_planes"]
self.num_cmd_type = params["num_cmd_type"]
self.mapx = params["map_x"]
self.mapy = params["map_y"]
out_dim = self.num_planes * self.mapx * self.mapy
# After trunk, we can predict the unit action and value.
# Four dimensions. unit loc, target loc,
self.unit_locs = nn.Conv2d(self.num_planes, 1, 3, padding=1)
self.target_locs = nn.Conv2d(self.num_planes, 1, 3, padding=1)
self.cmd_types = nn.Linear(out_dim, self.num_cmd_type)
self.build_types = nn.Linear(out_dim, self.num_unit)
self.value = nn.Linear(out_dim, 1)
self.relu = nn.LeakyReLU(0.1)
self.softmax = nn.Softmax()
def get_define_args():
return MiniRTSNet.get_define_args() + [
("ratio_skip_observation", 0.0),
("concat", dict(action="store_true")),
("enable_transition_model", dict(action="store_true")),
("gating", dict(action="store_true")),
]
def get_define_args():
return MiniRTSNet.get_define_args() + [
("ratio_skip_observation", 0.0),
("concat", dict(action="store_true")),
("enable_transition_model", dict(action="store_true")),
("gating", dict(action="store_true")),
]
def _init(self, args):
params = args.params
assert isinstance(params["num_action"], int), "num_action has to be a number. action = " + str(params["num_action"])
self.params = params
self.net = MiniRTSNet(args)
if self.params.get("model_no_spatial", False):
self.num_unit = params["num_unit_type"]
linear_in_dim = (params["num_unit_type"] + 7)
else:
linear_in_dim = (params["num_unit_type"] + 7) * 25
self.linear_policy = nn.Linear(linear_in_dim, params["num_action"])
self.linear_value = nn.Linear(linear_in_dim, 1)
self.softmax = nn.Softmax()
def _init(self, args):
params = args.params
assert isinstance(
params["num_action"],
int), "num_action has to be a number. action = " + str(
params["num_action"])
self.params = params
self.net = MiniRTSNet(args)
last_num_channel = self.net.num_channels[-1]
if self.params.get("model_no_spatial", False):
self.num_unit = params["num_unit_type"]
linear_in_dim = last_num_channel
else:
linear_in_dim = last_num_channel * 25
self.linear_value = nn.Linear(linear_in_dim, params["num_action"])
def _init(self, args):
params = args.params
assert isinstance(params["num_action"], int), "num_action has to be a number. action = " + str(params["num_action"])
self.params = params
self.net = MiniRTSNet(args)
if self.params.get("model_no_spatial", False):
self.num_unit = params["num_unit_type"]
linear_in_dim = (params["num_unit_type"] + 7)
else:
linear_in_dim = (params["num_unit_type"] + 7) * 25
self.na = params["num_action"]
self.linear_policy = nn.Linear(linear_in_dim, self.na)
self.linear_value = nn.Linear(linear_in_dim, 1)
self.relu = nn.LeakyReLU(0.1)
if self.args.concat:
self.Wt = nn.Linear(linear_in_dim * 2, linear_in_dim)
else:
self.Wt = nn.Linear(linear_in_dim, linear_in_dim)
self.sigmoid = nn.Sigmoid()
# Adaptive gating
if getattr(args, "gating", False):
self.gate_1 = nn.Linear(linear_in_dim, linear_in_dim)
self.gate_2 = nn.Linear(linear_in_dim, 1)
self.transition1 = nn.Linear(linear_in_dim + self.na, linear_in_dim)
self.transition2 = nn.Linear(linear_in_dim, linear_in_dim)
self.num_hidden_dim = linear_in_dim
self.softmax = nn.Softmax()
def get_define_args():
return MiniRTSNet.get_define_args()
def get_define_args():
return MiniRTSNet.get_define_args()