Reinforcement learning (RL) algorithms update an agent's parameters according
to one of several possible rules, discovered manually through years of
research. Automating the discovery of update rules from data could lead to more
efficient algorithms, or algorithms that are better adapted to specific
environments. Although there have been prior attempts at addressing this
significant scientific challenge, it remains an open question whether it is
feasible to discover alternatives to fundamental concepts of RL such as value
functions and temporal-difference learning. This paper introduces a new
meta-learning approach that discovers an entire update rule which includes both
'what to predict' (e.g. value functions) and 'how to learn from it' (e.g.
bootstrapping) by interacting with a set of environments. The output of this
method is an RL algorithm that we call Learned Policy Gradient (LPG). Empirical
results show that our method discovers its own alternative to the concept of
value functions. Furthermore it discovers a bootstrapping mechanism to maintain
and use its predictions. Surprisingly, when trained solely on toy environments,
LPG generalises effectively to complex Atari games and achieves non-trivial
performance. This shows the potential to discover general RL algorithms from
data.

该论文提出一种新的元学习方法，可以通过与一组环境交互，发现一个包含价值函数和时间差分学习等元素的更新规则，从而得到一个名为 LPG 的 RL 算法，该方法可以发现自己对于价值函数的替代方案，并有效地推广到复杂的 Atari 游戏中。