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 which
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, PPO, and SAC) in different kinds
of environments (Cartpole, Bipedal Walker 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. Our
code can be found at https://github.com/automl/AutoRL-Landscape