# Features¶

The features in MushroomRL are 1-D arrays computed applying a specified function to a raw input, e.g. polynomial features of the state of an MDP. MushroomRL supports three types of features:

• basis functions;
• tensor basis functions;
• tiles.

The tensor basis functions are a PyTorch implementation of the standard basis functions. They are less straightforward than the standard ones, but they are faster to compute as they can exploit parallel computing, e.g. GPU-acceleration and multi-core systems.

All the types of features are exposed by a single factory method Features that builds the one requested by the user.

mushroom_rl.features.features.Features(basis_list=None, tilings=None, tensor_list=None, n_outputs=None, function=None, device=None)[source]

Factory method to build the requested type of features. The types are mutually exclusive.

Possible features are tilings (tilings), basis functions (basis_list), tensor basis (tensor_list), and functional mappings (n_outputs and function).

The difference between basis_list and tensor_list is that the former is a list of python classes each one evaluating a single element of the feature vector, while the latter consists in a list of PyTorch modules that can be used to build a PyTorch network. The use of tensor_list is a faster way to compute features than basis_list and is suggested when the computation of the requested features is slow (see the Gaussian radial basis function implementation as an example). A functional mapping applies a function to the input computing an n_outputs-dimensional vector, where the mapping is expressed by function. If function is not provided, the identity is used.

Parameters: basis_list (list, None) – list of basis functions; tilings ([object, list], None) – single object or list of tilings; tensor_list (list, None) – list of dictionaries containing the instructions to build the requested tensors; n_outputs (int, None) – dimensionality of the feature mapping; function (object, None) – a callable function to be used as feature mapping. Only needed when using a functional mapping. device (int, None) – where to run the group of tensors. Only needed when using a list of tensors. The class implementing the requested type of features.
mushroom_rl.features.features.get_action_features(phi_state, action, n_actions)[source]

Compute an array of size len(phi_state) * n_actions filled with zeros, except for elements from len(phi_state) * action to len(phi_state) * (action + 1) that are filled with phi_state. This is used to compute state-action features.

Parameters: phi_state (np.ndarray) – the feature of the state; action (np.ndarray) – the action whose features have to be computed; n_actions (int) – the number of actions. The state-action features.

The factory method returns a class that extends the abstract class FeatureImplementation.

The documentation for every feature type can be found here: