We propose a novel AI challenge called a Cooperative Language Acquisition Problem (CLAP) in which a 'joiner' agent learns from a dataset of interactions between agents in a target community. We propose and compare two methods for solving CLAPs; Imitation Learning (IL), and Emergent Communication pretraining and Translation Learning (ECTL) for solving CLAPs.

Multicellularity is a remarkable form of complex organisation, and gene regulation is crucial to its evolution.

Blog post about our research presented at the 4th Emergent Communication Workshop at NeurIPS 2020.

In his 1991 book, *Consciousness Explained*, Daniel Dennett sketches a hypothetical, "just-so" story for the evolution of the "internal voice" involving the progressive developement of cognitive capabilities via "communicative autostimulation". In this post we explore these ideas and see if there is anything that we can learn for enhacing autocurricula in multiagent artificial intelligence.

In this tutorial we’re going to be implementing reinforcement learning (RL) agents to play games against one another. Before reading this it is advised to be familiar with the TF-Agents and Deep Q-Learning; this tutorial will bring you up to speed.

In this tutorial we use COCO captioned image data to build a model that produces natural language descriptions of given images.

In this post, Reddit user u/FlyingCow313 shows an image that resulted from thier attempt to implement a Mandlebrot renderer.