Multi-Step Generalized Policy Improvement by Leveraging Approximate Models Lucas N. Alegre 1, 2 Ana L. C. Bazzan 1 Ann Now é 2 Bruno C. da Silva 3 1

We introduce a principled method for performing zero-shot transfer in reinforcement learning (RL) by exploiting approximate models of the environment. Zero-shot transfer in RL has been investigated by leveraging methods rooted in generalized policy improvement (GPI) and successor features (SFs).