30 Dec 2020
Here is a bunch of brief informal impressions on the ideas behind some of the reinforcement learning papers I enjoyed at NeurIPS 2020. For the full story, including the experimental or theoretical results, I encourage you to check out the full works, that are linked. The common thread in all of these works? Some form of, explicit or implicit, model-based reinforcement learning.
30 Dec 2020
2020 was a difficult year for many. In difficult times, music, and art in general, has a very special role for society and individuals. To thank the artists that helped me and thousands of other music lovers go through this year, I want to give my impressions on my favorite eight albums from the last twelve months, restricting myself to three macro-genres that I enjoy, neoclassical, jazz and rock.
30 Apr 2020
There is a number of potential gains in using an approximate model in reinforcement learning, in terms, for instance, of safety and, as most commonly affirmed, sample-efficiency. However, there is an advantage that should not be forgotten and that is, perhaps, the most interesting: approximating the environment dynamics can unlock peculiar learning modalities that would be impossible in a model-free setting. We will see in this blog post that model-based techniques can be leveraged to obtain a critic that is tailor-made for policy optimization in an actor-critic setting. How? By allowing the critic to explicitly learn to produce accurate policy gradients.
26 Jan 2020
You recognized it. This is the super-handy expression for the policy gradient derived more than 20 years ago by Richard Sutton and colleagues. In this blog post, I will show how the Policy Gradient Theorem can offer a lens to interpret modern model-based policy search methods. Yes, even the ones that do not directly consider it.
31 Dec 2017
Luigi Pirandello was an outstanding author, whose writings shine in the history of European literature. Apart from a considerable number of plays and novels, he wrote also many novelle, (not too) short stories gathered in the collection Novelle per un anno (“Novelle for a year”).