Betreuer: Mohammd Karam Daaboul
Forschungsgruppe: Angewandte Technisch-Kognitive Systeme
Partner: FZI Forschungszentrum Informatik
Beginn: 26. Juni 2020
Reinforcement learning (RL) is a field of machine learning that deals with sequential decision-making aimed at maximizing a cumulative reward. Through the connection between RL and High-Capacity Representation like Neural Network, an AI-agent can learn to solve specific tasks from raw, low-level observations such as images. However, in continuous domains governed by complex dynamics, such as robotic control, it isn't easy to learn from these high-dimensional inputs directly. Standard model-free deep RL aims to use direct end-to-end training to unify these tasks of representation learning and task learning explicitly. However, solving both problems together is difficult, since an effective policy requires an adequate representation. The goal of this work is to use useful representations learned from the latent variable model and train effective RL agents in this learned latent space.