Recurrent neural networks, hidden states and beliefs in partially observable environments
Despite achieving impressive performances on various tasks, modern artificial intelligence (AI) systems have become complex black box models. A growing body of work aspires to open the box and understand its internal functioning. In this new article (Lambrechts et al., 2022), we follow this field of research by studying the internal representation that intelligent agents learn through reinforcement learning (RL), when those agents act in partially observable environments (POEs). In particular, the informational content of the memory of those agents is studied when the latter are trained to act optimally in maze and orientation tasks.