Planning with learned ignorance-aware models - Filos, A (2022)
One of the goals of artificial intelligence research is to create decision-makers (i.e., agents) that improve from experience (i.e., data), collected through interaction with an environment. Models of the environment (i.e., world models) are an explicit way that agents use to represent their knowledge, enabling them to make counterfactual predictions and plans without requiring additional environment interactions. Although agents that plan with a perfect model of the environment have led to impressive demonstrations, e.g., super- human performance in board games, they are limited to problems their designer can specify a perfect model. Therefore, learning models from experience holds the promise of going beyond the scope of their designers’ reach, giving rise to a self-improving vicious circle of (i) learning a model from the past experience; (ii) planning with the learned model; and (iii) interacting with the environment, collecting new experiences. Ideally, learned models should generalise to situations beyond their training regime. Nonetheless, this is ambitious and often unrealistic when finite data is used for learning the models, leading to generally imperfect models, with which naive planning could be catastrophic in novel, out-of-training distribution situations. A more pragmatic goal is to have agents that are aware of and quantify their lack of knowledge (i.e., ignorance or epistemic uncertainty).