Although Reinforcement Learning (RL) has shown to be capable of producing
impressive results, its use is limited by the impact of its hyperparameters on
performance. This often makes it difficult to achieve good results in practice.
Automated RL (AutoRL) addresses this difficulty, yet little is known about the
dynamics of the hyperparameter landscapes that hyperparameter optimization
(HPO) methods traverse in search of optimal configurations. In view of existing
AutoRL approaches dynamically adjusting hyperparameter configurations, we
propose an approach to build and analyze these hyperparameter landscapes not
just for one point in time but at multiple points in time throughout training.
Addressing an important open question on the legitimacy of such dynamic AutoRL
approaches, we provide thorough empirical evidence that the hyperparameter
landscapes strongly vary over time across representative algorithms from RL
literature (DQN and SAC) in different kinds of environments (Cartpole and
Hopper). This supports the theory that hyperparameters should be dynamically
adjusted during training and shows the potential for more insights on AutoRL
problems that can be gained through landscape analyses.

该研究提出了一种动态分析强化学习中超参数优化过程中的超参数地形特征的方法，并在实验中得到了支持，进一步说明超参数应该在训练过程中进行动态调整。

AutoRL 超参数景观

AutoRL Hyperparameter Landscapes

Reinforcement Learning and recently Deep Reinforcement Learning are popular
methods for solving sequential decision making problems modeled as Markov
Decision Processes. RL modeling of a problem and selecting algorithms and
hyper-parameters require careful considerations as different configurations may
entail completely different performances. These considerations are mainly the
task of RL experts; however, RL is progressively becoming popular in other
fields where the researchers and system designers are not RL experts. Besides,
many modeling decisions, such as defining state and action space, size of
batches and frequency of batch updating, and number of timesteps are typically
made manually. For these reasons, automating different components of RL
framework is of great importance and it has attracted much attention in recent
years. Automated RL provides a framework in which different components of RL
including MDP modeling, algorithm selection and hyper-parameter optimization
are modeled and defined automatically. In this article, we explore the
literature and present recent work that can be used in automated RL. Moreover,
we discuss the challenges, open questions and research directions in AutoRL.

论文探讨自动化强化学习的关键元素和挑战，并讨论自动化强化学习的最新工作、应用和研究方向。