Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents
This work addresses the challenge of improving control algorithms for embodied and situated agents, but it is incremental as it builds on prior studies with additional experiments and comparisons.
The paper tackles the problem of enhancing continuous control optimization by integrating a self-supervised feature extraction module, showing that this approach improves performance beyond dimensionality reduction and allocentric perception, with sequence-to-sequence learning yielding better results than previous methods.
As discussed in previous studies, the efficacy of evolutionary or reinforcement learning algorithms for continuous control optimization can be enhanced by including a neural module dedicated to feature extraction trained through self-supervised methods. In this paper we report additional experiments supporting this hypothesis and we demonstrate how the advantage provided by feature extraction is not limited to problems that benefit from dimensionality reduction or that involve agents operating on the basis of allocentric perception. We introduce a method that permits to continue the training of the feature-extraction module during the training of the policy network and that increases the efficacy of feature extraction. Finally, we compare alternative feature-extracting methods and we show that sequence-to-sequence learning yields better results than the methods considered in previous studies.