Speaker
Description
Recent years have seen a surge of interest in evolutionary reinforcement learning (evoRL), where evolutionary computation techniques are used to tackle reinforcement learning (RL) tasks. Naturally, many of the existing ideas from meta-RL can also be applied in this context. This is particularly important when handling dynamic (non-stationary) RL environments, where agents need to respond swiftly to changes (shifts) in the environment. We will discuss several research paths aimed at integrating meta-RL with evoRL, particularly those that leverage less orthodox principles, such as evolvability and higher-order meta-learning through meta-mutation rates. The incorporation of these principles is expected to lead to greater sample efficiency when dealing with dynamic and/or noisy RL environments, which are typical of most real-life RL applications.
