mirror of
https://github.com/vale981/ray
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78 lines
2.8 KiB
Python
78 lines
2.8 KiB
Python
from ray.rllib.models.modelv2 import ModelV2
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from ray.rllib.models.tf.tf_modelv2 import TFModelV2
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from ray.rllib.models.torch.misc import SlimFC
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from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.framework import try_import_tf, try_import_torch
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tf1, tf, tfv = try_import_tf()
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torch, nn = try_import_torch()
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class FastModel(TFModelV2):
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"""An example for a non-Keras ModelV2 in tf that learns a single weight.
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Defines all network architecture in `forward` (not `__init__` as it's
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usually done for Keras-style TFModelV2s).
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"""
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def __init__(self, obs_space, action_space, num_outputs, model_config,
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name):
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super().__init__(obs_space, action_space, num_outputs, model_config,
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name)
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# Have we registered our vars yet (see `forward`)?
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self._registered = False
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@override(ModelV2)
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def forward(self, input_dict, state, seq_lens):
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with tf1.variable_scope("model", reuse=tf1.AUTO_REUSE):
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bias = tf1.get_variable(
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dtype=tf.float32,
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name="bias",
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initializer=tf.keras.initializers.Zeros(),
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shape=())
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output = bias + \
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tf.zeros([tf.shape(input_dict["obs"])[0], self.num_outputs])
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self._value_out = tf.reduce_mean(output, -1) # fake value
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if not self._registered:
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self.register_variables(
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tf1.get_collection(
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tf1.GraphKeys.TRAINABLE_VARIABLES, scope=".+/model/.+"))
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self._registered = True
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return output, []
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@override(ModelV2)
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def value_function(self):
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return tf.reshape(self._value_out, [-1])
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class TorchFastModel(TorchModelV2, nn.Module):
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"""Torch version of FastModel (tf)."""
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def __init__(self, obs_space, action_space, num_outputs, model_config,
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name):
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TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
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model_config, name)
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nn.Module.__init__(self)
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self.bias = nn.Parameter(
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torch.tensor([0.0], dtype=torch.float32, requires_grad=True))
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# Only needed to give some params to the optimizer (even though,
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# they are never used anywhere).
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self.dummy_layer = SlimFC(1, 1)
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self._output = None
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@override(ModelV2)
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def forward(self, input_dict, state, seq_lens):
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self._output = self.bias + torch.zeros(
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size=(input_dict["obs"].shape[0], self.num_outputs)).to(
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self.bias.device)
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return self._output, []
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@override(ModelV2)
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def value_function(self):
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assert self._output is not None, "must call forward first!"
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return torch.reshape(torch.mean(self._output, -1), [-1])
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