Topic: composite task

The ability to compose new skills from a pre-acquired behavior repertoire is a hallmark of intelligence in humans and other animals. In deep reinforcement learning (RL), artificial agents can extract re-usable skills from past experience and recombine them in a hierarchical manner. It remains largely unknown, however, whether the brain similarly composes a novel behavior. Here we trained deep RL agents with the soft actor-critic (SAC) algorithm and studied their representation of RL variables during hierarchical learning. The objective of SAC is to maximize future cumulative rewards and policy