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https://github.com/vale981/ray
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43043ee4d5
* WIP. * Fixes. * LINT. * WIP. * WIP. * Fixes. * Fixes. * Fixes. * Fixes. * WIP. * Fixes. * Test * Fix. * Fixes and LINT. * Fixes and LINT. * LINT.
69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
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from ray.rllib.utils.framework import try_import_tf
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tf1, tf, tfv = try_import_tf()
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class SimpleQModel(TFModelV2):
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"""Extension of standard TFModel to provide Q values.
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Data flow:
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obs -> forward() -> model_out
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model_out -> get_q_values() -> Q(s, a)
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Note that this class by itself is not a valid model unless you
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implement forward() in a subclass."""
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def __init__(self,
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obs_space,
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action_space,
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num_outputs,
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model_config,
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name,
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q_hiddens=(256, )):
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"""Initialize variables of this model.
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Extra model kwargs:
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q_hiddens (list): defines size of hidden layers for the q head.
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These will be used to postprocess the model output for the
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purposes of computing Q values.
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Note that the core layers for forward() are not defined here, this
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only defines the layers for the Q head. Those layers for forward()
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should be defined in subclasses of SimpleQModel.
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"""
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super(SimpleQModel, self).__init__(obs_space, action_space,
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num_outputs, model_config, name)
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# setup the Q head output (i.e., model for get_q_values)
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self.model_out = tf.keras.layers.Input(
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shape=(num_outputs, ), name="model_out")
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if q_hiddens:
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last_layer = self.model_out
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for i, n in enumerate(q_hiddens):
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last_layer = tf.keras.layers.Dense(
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n, name="q_hidden_{}".format(i),
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activation=tf.nn.relu)(last_layer)
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q_out = tf.keras.layers.Dense(
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action_space.n, activation=None, name="q_out")(last_layer)
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else:
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q_out = self.model_out
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self.q_value_head = tf.keras.Model(self.model_out, q_out)
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self.register_variables(self.q_value_head.variables)
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def get_q_values(self, model_out):
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"""Returns Q(s, a) given a feature tensor for the state.
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Override this in your custom model to customize the Q output head.
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Arguments:
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model_out (Tensor): embedding from the model layers
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Returns:
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action scores Q(s, a) for each action, shape [None, action_space.n]
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"""
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return self.q_value_head(model_out)
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