AutoML.org
Freiburg-Hannover-TübingenThe lack of widespread adoption of AutoML tools in the broader ML community has been a recurring topic of discussion within the field. Is this due to a lack of trust in these systems? Do our benchmarks fail to reflect real-world use cases? Or is it simply too difficult to find and implement state-of-the-art methods? […]
Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, Sebastian Döhler, André Biedenkapp, Bodo Rosenhahn, Frank Hutter and Marius Lindauer TLDR: We can model and investigate generalization in RL with contextual RL and our benchmark library CARL. In theory, without adding context we cannot achieve optimal performance and in the experiments we saw that using context […]
Hyperparameter Optimization tools perform well on Reinforcement Learning, outperforming Grid Searches with less than 10% of the budget. If not reported correctly, however, all hyperparameter tuning can heavily skew future comparisons.
RL agents, just like humans, often benefit from a difficulty curve in learning [Matiisen et al. 2017, Fuks et al. 2019, Zhang et al. 2020]. Progressing from simple task instances, e.g. walking on flat surfaces or towards goals that are very close to the agent, to more difficult ones lets the agent accomplish much harder […]
Dynamic Algorithm Configuration (DAC) has been shown to significantly improve algorithm performance over static or even handcrafted dynamic hyperparameter policies [Biedenkapp et al., 2020]. Most algorithms, however, are not designed with DAC in mind and have to be adapted to be controlled online. This requires a great deal of familiarity with the target algorithm as […]