From Pixels to Torques: Policy Learning with Deep Dynamical Models
This addresses the challenge of data-efficient learning in continuous state-action spaces with high-dimensional observations for autonomous systems, representing an incremental improvement over existing methods.
The paper tackles the pixels to torques problem, where an agent learns a closed-loop control policy from pixel observations, by introducing a data-efficient, model-based reinforcement learning algorithm that uses a deep dynamical model for joint learning of low-dimensional embeddings and predictive models, resulting in quick learning and scalability to high-dimensional state spaces.
Data-efficient learning in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. In this paper, we consider one instance of this challenge, the pixels to torques problem, where an agent must learn a closed-loop control policy from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information. The key ingredient is a deep dynamical model that uses deep auto-encoders to learn a low-dimensional embedding of images jointly with a predictive model in this low-dimensional feature space. Joint learning ensures that not only static but also dynamic properties of the data are accounted for. This is crucial for long-term predictions, which lie at the core of the adaptive model predictive control strategy that we use for closed-loop control. Compared to state-of-the-art reinforcement learning methods for continuous states and actions, our approach learns quickly, scales to high-dimensional state spaces and is an important step toward fully autonomous learning from pixels to torques.