by Daniel Jost, Basavasagar Patil, Xavier Alameda-Pineda, and Chris Reinke [preprint] | [code] Abstract: Deep Neural Networks (DNNs) became the standard tool for function approximation with most of the introduced architectures being developed for high-dimensional input data. However, many real-world problems have low-dimensional inputs for which the standard Multi-Layer Perceptron…
by Anand Ballou, Xavier Alameda-Pineda, and Chris Reinke Applied Intelligence [paper][code] Abstract: With the increasing presence of robots in our everyday environments, improving their social skills is of utmost importance. Nonetheless, social robotics still faces many challenges. One bottleneck is that robotic behaviors often need to be adapted, as social…
by Chris Reinke and Xavier Alameda-Pineda Transactions on Machine Learning Research [Paper][Code] Abstract. Transfer in Reinforcement Learning aims to improve learning performance on target tasks using knowledge from experienced source tasks. Successor features (SF) are a prominent transfer mechanism in domains where the reward function changes between tasks. They reevaluate…
by Davier Emukpere, Xavier Alameda-Pineda and Chris Reinke [Paper][code] Abstract. A longstanding goal in reinforcement learning is to build intelligent agents that show fast learning and a flexible transfer of skills akin to humans and animals. This paper investigates the integration of two frameworks for tackling those goals: episodic control and successor features. Episodic…
