Add FARE recourse method.#199
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* Add suitable pytest with a MockEnv * Add fare_actions_factory to easily generate action sets * Add sml-unitn/recourse-fare as dependency [1] De Toni, Giovanni, Bruno Lepri, and Andrea Passerini. "Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis." Machine Learning (2023): 1-21.
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This PR is an initial attempt to add the following two novel recourse methods to the library, FARE and E-FARE [1]. Differently from other approaches, FARE couples a reinforcement learning agent and a discrete search procedure, MCTS, to efficiently discover counterfactual interventions.
Currently, I have added FARE and a simple pytest to show its usage. Some class/function documentation is still missing, but I would like to start a discussion about what would be the best strategy to proceed.
This PR also adds as a dependency the sml-unitn/recourse-fare repository where the original code is located (in order to avoid bloating CARLA with additional files). Obviously, this can be changed.
Minor additions:
[1] De Toni, Giovanni, Bruno Lepri, and Andrea Passerini. "Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis." Machine Learning (2023): 1-21. doi:10.1007/s10994-022-06293-7