Reusable Reinforcement Learning for Modular Self Motivated Agents
Jaroslav Vítků, Pavel Nahodil · 2014
Presented topic is from the research fields called Artificial Life and Artificial Intelligence (AI). In this paper, there is presented novel approach to designing agent architectures with its requirements. The approach is inspired by inherited modularity of biological brains and agent architectures are represented here as set of given reusable modules connected into a particular topology. This paper presents design of two particular modules for future use in more complex architectures. The modules are used for implementing model-free motivation-driven Reinforcement Learning (RL). First, the novel framework for these architectures is described together with a used simulator. Then, the design of two new reusable domain-independent components of agent architectures is described. Finally, experimental validation of these new components and their future use is mentioned.