1 research outputs found
AutoRL Hyperparameter Landscapes
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