garage.torch.policies.categorical_cnn_policy
¶
CategoricalCNNPolicy.
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class
CategoricalCNNPolicy
(env, kernel_sizes, hidden_channels, strides=1, hidden_sizes=(32, 32), hidden_nonlinearity=torch.tanh, hidden_w_init=nn.init.xavier_uniform_, hidden_b_init=nn.init.zeros_, paddings=0, padding_mode='zeros', max_pool=False, pool_shape=None, pool_stride=1, output_nonlinearity=None, output_w_init=nn.init.xavier_uniform_, output_b_init=nn.init.zeros_, layer_normalization=False, name='CategoricalCNNPolicy')¶ Bases:
garage.torch.policies.stochastic_policy.StochasticPolicy
CategoricalCNNPolicy.
A policy that contains a CNN and a MLP to make prediction based on a categorical distribution.
It only works with akro.Discrete action space.
Parameters: - env (garage.envs) – Environment.
- kernel_sizes (tuple[int]) – Dimension of the conv filters. For example, (3, 5) means there are two convolutional layers. The filter for first layer is of dimension (3 x 3) and the second one is of dimension (5 x 5).
- strides (tuple[int]) – The stride of the sliding window. For example, (1, 2) means there are two convolutional layers. The stride of the filter for first layer is 1 and that of the second layer is 2.
- hidden_channels (tuple[int]) – Number of output channels for CNN. For example, (3, 32) means there are two convolutional layers. The filter for the first conv layer outputs 3 channels
- hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for mean. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
- hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a torch.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 torch.Tensor.
- hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a torch.Tensor.
- paddings (tuple[int]) – Zero-padding added to both sides of the input
- padding_mode (str) – The type of padding algorithm to use, either ‘SAME’ or ‘VALID’.
- max_pool (bool) – Bool for using max-pooling or not.
- pool_shape (tuple[int]) – Dimension of the pooling layer(s). For example, (2, 2) means that all the pooling layers have shape (2, 2).
- pool_stride (tuple[int]) – The strides of the pooling layer(s). For example, (2, 2) means that all the pooling layers have strides (2, 2).
- output_nonlinearity (callable) – Activation function for output dense layer. It should return a torch.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 torch.Tensor.
- output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a torch.Tensor.
- layer_normalization (bool) – Bool for using layer normalization or not.
- name (str) – Name of policy.
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env_spec
¶ Policy environment specification.
Returns: Environment specification. Return type: garage.EnvSpec
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observation_space
¶ Observation space.
Returns: The observation space of the environment. Return type: akro.Space
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action_space
¶ Action space.
Returns: The action space of the environment. Return type: akro.Space
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forward
(self, observations)¶ Compute the action distributions from the observations.
Parameters: observations (torch.Tensor) – Batch of observations on default torch device. Returns: Batch distribution of actions. dict[str, torch.Tensor]: Additional agent_info, as torch Tensors. Do not need to be detached, and can be on any device.Return type: torch.distributions.Distribution
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get_action
(self, observation)¶ Get a single action given an observation.
Parameters: observation (np.ndarray) – Observation from the environment. Shape is \(env_spec.observation_space\). Returns: - np.ndarray: Predicted action. Shape is
- \(env_spec.action_space\).
- dict:
- np.ndarray[float]: Mean of the distribution
- np.ndarray[float]: Standard deviation of logarithmic
- values of the distribution.
Return type: tuple
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get_actions
(self, observations)¶ Get actions given observations.
Parameters: observations (np.ndarray) – Observations from the environment. Shape is \(batch_dim \bullet env_spec.observation_space\). Returns: - np.ndarray: Predicted actions.
- \(batch_dim \bullet env_spec.action_space\).
- dict:
- np.ndarray[float]: Mean of the distribution.
- np.ndarray[float]: Standard deviation of logarithmic
- values of the distribution.
Return type: tuple
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get_param_values
(self)¶ Get the parameters to the policy.
This method is included to ensure consistency with TF policies.
Returns: The parameters (in the form of the state dictionary). Return type: dict
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set_param_values
(self, state_dict)¶ Set the parameters to the policy.
This method is included to ensure consistency with TF policies.
Parameters: state_dict (dict) – State dictionary.
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reset
(self, do_resets=None)¶ Reset the policy.
This is effective only to recurrent policies.
do_resets is an array of boolean indicating which internal states to be reset. The length of do_resets should be equal to the length of inputs, i.e. batch size.
Parameters: do_resets (numpy.ndarray) – Bool array indicating which states to be reset.