def __init__(self, num_inputs, obs_space, recurrent=False, hidden_size=32):
super(MLPBase, self).__init__(recurrent, num_inputs, hidden_size)
if recurrent:
num_inputs = hidden_size
init_ = lambda m: init(m, nn.init.orthogonal_, lambda x: nn.init.
constant_(x, 0), np.sqrt(2))
# self.actor = nn.Sequential(
# init_(nn.Linear(num_inputs, hidden_size)), nn.Tanh(),
# init_(nn.Linear(hidden_size, hidden_size)), nn.Tanh())
# self.critic = nn.Sequential(
# init_(nn.Linear(num_inputs, hidden_size)), nn.Tanh(),
# init_(nn.Linear(hidden_size, hidden_size)), nn.Tanh())
self.actor = MLP(
num_inputs,
32,
[16, 32, 64]
)
self.critic = MLP(
num_inputs,
32,
[16, 32, 64]
)
self.critic_linear = init_(nn.Linear(32, 1))
self.train()
def __init__(self, environment):
super().__init__()
observation_space = environment.observation_space.shape
image_size = CHW(observation_space[2], observation_space[0],
observation_space[1])
self.conv = SimpleConvNet(image_size.channels, 1, [], [3],
{"USE_BATCH_NORM": True})
self.mlp = MLP(
self.conv.output_size((image_size.width, image_size.height)), 32,
[32, 32])
def __init__(self, environment):
super().__init__()
self.mlp = MLP(np.prod(environment.observation_space.shape), 32,
[16, 32, 64])
def create_empty(self, environment, policy):
return datastructures.Ensemble([
MLP(32 + get_action_space_size(environment.action_space), 32,
[32, 32]) for i in range(self.NUM_MODELS)
], environment).to(DefaultDevice.current())
def create_empty(self, environment, policy):
return MLP(32, 32, [32, 32]).to(DefaultDevice.current())
def create_empty(self, environment, policy):
return MLP(get_action_space_size(environment.action_space), 32,
[32, 32]).to(DefaultDevice.current())