Online Weighted Q-Ensembles for Reduced Hyperparameter Tuning in Reinforcement Learning

• URL: http://arxiv.org/abs/2209.15078v1
• Date: Thu, 29 Sep 2022 19:57:43 GMT
• Title: Online Weighted Q-Ensembles for Reduced Hyperparameter Tuning in Reinforcement Learning
• Authors: Renata Garcia and Wouter Caarls
• Abstract summary: Reinforcement learning is a promising paradigm for learning robot control, allowing complex control policies to be learned without requiring a dynamics model. We propose employing an ensemble of multiple reinforcement learning agents, each with a different set of hyper parameters, along with a mechanism for choosing the best performing set. Online weighted Q-Ensemble presented overall lower variance and superior results when compared with q-average ensembles.
• Score: 0.38073142980732994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning is a promising paradigm for learning robot control, allowing complex control policies to be learned without requiring a dynamics model. However, even state of the art algorithms can be difficult to tune for optimum performance. We propose employing an ensemble of multiple reinforcement learning agents, each with a different set of hyperparameters, along with a mechanism for choosing the best performing set(s) on-line. In the literature, the ensemble technique is used to improve performance in general, but the current work specifically addresses decreasing the hyperparameter tuning effort. Furthermore, our approach targets on-line learning on a single robotic system, and does not require running multiple simulators in parallel. Although the idea is generic, the Deep Deterministic Policy Gradient was the model chosen, being a representative deep learning actor-critic method with good performance in continuous action settings but known high variance. We compare our online weighted q-ensemble approach to q-average ensemble strategies addressed in literature using alternate policy training, as well as online training, demonstrating the advantage of the new approach in eliminating hyperparameter tuning. The applicability to real-world systems was validated in common robotic benchmark environments: the bipedal robot half cheetah and the swimmer. Online Weighted Q-Ensemble presented overall lower variance and superior results when compared with q-average ensembles using randomized parameterizations.

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