Reinforcement learning agents deployed in the real world often have to cope
with partially observable environments. Therefore, most agents employ memory
mechanisms to approximate the state of the environment. Recently, there have
been impressive success stories in mastering partially observable environments,
mostly in the realm of computer games like Dota 2, StarCraft II, or MineCraft.
However, existing methods lack interpretability in the sense that it is not
comprehensible for humans what the agent stores in its memory. In this regard,
we propose a novel memory mechanism that represents past events in human
language. Our method uses CLIP to associate visual inputs with language tokens.
Then we feed these tokens to a pretrained language model that serves the agent
as memory and provides it with a coherent and human-readable representation of
the past. We train our memory mechanism on a set of partially observable
environments and find that it excels on tasks that require a memory component,
while mostly attaining performance on-par with strong baselines on tasks that
do not. On a challenging continuous recognition task, where memorizing the past
is crucial, our memory mechanism converges two orders of magnitude faster than
prior methods. Since our memory mechanism is human-readable, we can peek at an
agent's memory and check whether crucial pieces of information have been
stored. This significantly enhances troubleshooting and paves the way toward
more interpretable agents.Comment: To appear at NeurIPS 2023, 10 pages (+ references and appendix),
Code: https://github.com/ml-jku/hel