garage.tf.policies.categorical_lstm_policy module¶
CategoricalLSTMPolicy with model.
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class
CategoricalLSTMPolicy
(env_spec, name='CategoricalLSTMPolicy', hidden_dim=32, hidden_nonlinearity=<function tanh>, hidden_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, hidden_b_init=<tensorflow.python.ops.init_ops.Zeros object>, recurrent_nonlinearity=<function sigmoid>, recurrent_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, output_nonlinearity=<function softmax>, output_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, output_b_init=<tensorflow.python.ops.init_ops.Zeros object>, hidden_state_init=<tensorflow.python.ops.init_ops.Zeros object>, hidden_state_init_trainable=False, cell_state_init=<tensorflow.python.ops.init_ops.Zeros object>, cell_state_init_trainable=False, state_include_action=True, forget_bias=True, layer_normalization=False)[source]¶ Bases:
garage.tf.policies.base.StochasticPolicy
A policy that contains a LSTM to make prediction based on a categorical distribution.
It only works with akro.Discrete action space.
Parameters: - env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
- name (str) – Policy name, also the variable scope.
- hidden_dim (int) – Hidden dimension for LSTM cell.
- hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation.
- hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor.
- hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor.
- recurrent_nonlinearity (callable) – Activation function for recurrent layers. It should return a tf.Tensor. Set it to None to maintain a linear activation.
- recurrent_w_init (callable) – Initializer function for the weight of recurrent layer(s). The function should return a tf.Tensor.
- output_nonlinearity (callable) – Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation.
- output_w_init (callable) – Initializer function for the weight of output dense layer(s). The function should return a tf.Tensor.
- output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor.
- hidden_state_init (callable) – Initializer function for the initial hidden state. The functino should return a tf.Tensor.
- hidden_state_init_trainable (bool) – Bool for whether the initial hidden state is trainable.
- cell_state_init (callable) – Initializer function for the initial cell state. The functino should return a tf.Tensor.
- cell_state_init_trainable (bool) – Bool for whether the initial cell state is trainable.
- state_include_action (bool) – Whether the state includes action. If True, input dimension will be (observation dimension + action dimension).
- forget_bias (bool) – If True, add 1 to the bias of the forget gate at initialization. It’s used to reduce the scale of forgetting at the beginning of the training.
- layer_normalization (bool) – Bool for using layer normalization or not.
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distribution
¶ Policy distribution.
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recurrent
¶ Recurrent or not.
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state_info_specs
¶ State info specification.
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vectorized
¶ Vectorized or not.