no code implementations • 18 Oct 2021 • Kurtland Chua, Qi Lei, Jason D. Lee
To address this gap, we analyze HRL in the meta-RL setting, where a learner learns latent hierarchical structure during meta-training for use in a downstream task.
no code implementations • NeurIPS 2021 • Kurtland Chua, Qi Lei, Jason D. Lee
Representation learning has been widely studied in the context of meta-learning, enabling rapid learning of new tasks through shared representations.
1 code implementation • 26 Feb 2021 • Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra
We demonstrate that this problem can be tackled effectively with automated HPO, which we demonstrate to yield significantly improved performance compared to human experts.
Hyperparameter Optimization Model-based Reinforcement Learning +2
no code implementations • 27 Sep 2018 • Kurtland Chua, Rowan Mcallister, Roberto Calandra, Sergey Levine
We show that both challenges can be addressed by representing model-uncertainty, which can both guide exploration in the unsupervised phase and ensure that the errors in the model are not exploited by the planner in the goal-directed phase.
Model-based Reinforcement Learning reinforcement-learning +1
10 code implementations • NeurIPS 2018 • Kurtland Chua, Roberto Calandra, Rowan Mcallister, Sergey Levine
Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 10 Sep 2017 • Somil Bansal, Roberto Calandra, Kurtland Chua, Sergey Levine, Claire Tomlin
Reinforcement Learning is divided in two main paradigms: model-free and model-based.