def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
model_config, name)
nn.Module.__init__(self)
filters = model_config.get("conv_filters")
if not filters:
filters = _get_filter_config(obs_space.shape)
layers = []
(w, h, in_channels) = obs_space.shape
in_size = [w, h]
for out_channels, kernel, stride in filters[:-1]:
padding, out_size = valid_padding(in_size, kernel,
[stride, stride])
layers.append(
SlimConv2d(in_channels, out_channels, kernel, stride, padding))
in_channels = out_channels
in_size = out_size
out_channels, kernel, stride = filters[-1]
layers.append(
SlimConv2d(in_channels, out_channels, kernel, stride, None))
self._convs = nn.Sequential(*layers)
self._logits = SlimFC(out_channels,
num_outputs,
initializer=nn.init.xavier_uniform_)
self._value_branch = SlimFC(out_channels,
1,
initializer=normc_initializer())
self._cur_value = None
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
model_config, name)
nn.Module.__init__(self)
activation = get_activation_fn(
model_config.get("conv_activation"), framework="torch")
filters = model_config.get("conv_filters")
if not filters:
filters = _get_filter_config(obs_space.shape)
# no_final_linear = model_config.get("no_final_linear")
# vf_share_layers = model_config.get("vf_share_layers")
layers = []
(w, h, in_channels) = obs_space.shape
in_size = [w, h]
for out_channels, kernel, stride in filters[:-1]:
padding, out_size = valid_padding(in_size, kernel,
[stride, stride])
layers.append(
SlimConv2d(
in_channels,
out_channels,
kernel,
stride,
padding,
activation_fn=activation))
in_channels = out_channels
in_size = out_size
out_channels, kernel, stride = filters[-1]
layers.append(
SlimConv2d(
in_channels,
out_channels,
kernel,
stride,
None,
activation_fn=activation))
self._convs = nn.Sequential(*layers)
self._logits = SlimFC(
out_channels, num_outputs, initializer=nn.init.xavier_uniform_)
self._value_branch = SlimFC(
out_channels, 1, initializer=normc_initializer())
# Holds the current "base" output (before logits layer).
self._features = None
