Bayesian Nonparametric Feature and Policy Learning for Decision-Making
This work addresses the challenge of reasoning about observed behavior in learning from demonstrations, which is incremental as it builds on existing approaches by incorporating latent feature inference.
The paper tackles the problem of learning decision-making from demonstrations by proposing a generative model that infers latent features and policies, enabling action prediction for new states. Simulations and a real-world driver behavior study demonstrate the algorithm's performance.
Learning from demonstrations has gained increasing interest in the recent past, enabling an agent to learn how to make decisions by observing an experienced teacher. While many approaches have been proposed to solve this problem, there is only little work that focuses on reasoning about the observed behavior. We assume that, in many practical problems, an agent makes its decision based on latent features, indicating a certain action. Therefore, we propose a generative model for the states and actions. Inference reveals the number of features, the features, and the policies, allowing us to learn and to analyze the underlying structure of the observed behavior. Further, our approach enables prediction of actions for new states. Simulations are used to assess the performance of the algorithm based upon this model. Moreover, the problem of learning a driver's behavior is investigated, demonstrating the performance of the proposed model in a real-world scenario.