先说答案，学到了但也没学到。 这篇文章的标题里有两个概念，一个

I’ve been asked by quite a few people about what “language grounding” means. So I think I’ll write a short post explaining it, and specifically arguing why it is important to a truly intelligent agent.

Recently, I was working on a project that requires learning a latent representation with disentangled factors for high-dimensional inputs. For a brief introduction to disentanglement, while we could use an autoencoder (AE) to compress a high-dimensional input into a compact embedding, there is always dependence among the embedding dimensions, meaning that multiple dimensions always change together in a dependent way.

The other day I was reading the article “You and your research”, transcribed from a seminar by Richard Hamming. There is one paragraph about “choosing important problems” which I think is inspirational:

Assume that we have a policy $\pi(a_{0:k-1}|s_0)$ that is able to output a variable length $k$ of actions given any state $s_0$. We define a Q function $Q(s_0,a_{0:k-1})$ which computes the state-action value of any action sequence $a_0,a_1,\ldots,a_{k-1}$ given $s_0$.