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add dynamics lr

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wassname
2018-01-19 12:08:32 +08:00
parent a87a3ad7bb
commit 201fa2400f
3 changed files with 57 additions and 15 deletions
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ddpg/nets.py
+23 -6
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@@ -154,19 +154,18 @@ class CriticHead(nn.Module):
return x
class ActorHead(nn.Module):
def __init__(self, base, n_observation, n_action,
layers, activation=torch.nn.ELU,
layer_norm=False,
parameters_noise=False, parameters_noise_factorised=False,
last_activation=torch.nn.Tanh, init_w=3e-3):
last_activation=lambda x: x, init_w=3e-3):
super(ActorHead, self).__init__()
self.base = base
self.policy_net = LinearNet(
layers=[self.base.feature_net.output_shape, n_action],
activation=last_activation,
# activation=last_activation,
layer_norm=False
)
self.init_weights(init_w)
@@ -183,7 +182,6 @@ class ActorHead(nn.Module):
return x
class DynamicsHead(nn.Module):
def __init__(self, base, n_observation, n_action,
layers, activation=torch.nn.ELU,
@@ -192,15 +190,34 @@ class DynamicsHead(nn.Module):
init_w=3e-3):
super(DynamicsHead, self).__init__()
self.base = base
self.value_net = nn.Linear(self.base.feature_net.output_shape + n_action, n_observation)
if parameters_noise:
def linear_layer(x_in, x_out):
return NoisyLinear(x_in, x_out, factorised=parameters_noise_factorised)
else:
linear_layer = nn.Linear
# self.value_net = nn.Linear(self.base.feature_net.output_shape + n_action, self.base.feature_net.output_shape)
self.value_net = LinearNet(
layers=[self.base.feature_net.output_shape + n_action, self.base.feature_net.output_shape],
activation=activation,
layer_norm=layer_norm,
linear_layer=linear_layer
)
self.value_net2 = nn.Linear(self.base.feature_net.output_shape, n_observation)
self.init_weights(init_w)
def init_weights(self, init_w):
self.value_net.weight.data.uniform_(-init_w, init_w)
self.value_net2.weight.data.uniform_(-init_w, init_w)
# self.value_net.weight.data.uniform_(-init_w, init_w)
for layer in self.value_net.net:
if isinstance(layer, nn.Linear):
layer.weight.data.uniform_(-init_w, init_w)
def forward(self, observation, action):
action = to_torch_variable(action)
x = self.base.forward(observation)
x = torch.cat((x, action), dim=1)
x = self.value_net.forward(x)
x = self.value_net2.forward(x)
return x