Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control

Trial-and-error based reinforcement learning (RL) has seen rapid advancements in recent times, especially with the advent of deep neural networks. However, the majority of autonomous RL algorithms either rely on engineered features or a large number of interactions with the environment. Such a large number of interactions may be impractical in many real-world applications. For example, robots are subject to wear and tear and, hence, millions of interactions may change or damage the system. Moreover, practical systems have limitations in the form of the maximum torque that can be safely applied. To reduce the number of system interactions while naturally handling constraints, we propose a model-based RL framework based on Model Predictive Control (MPC). In particular, we propose to learn a probabilistic transition model using Gaussian Processes (GPs) to incorporate model uncertainties into long-term predictions, thereby, reducing the impact of model errors. We then use MPC to find a control sequence that minimises the expected long-term cost. We provide theoretical guarantees for the first-order optimality in the GP-based transition models with deterministic approximate inference for long-term planning. The proposed framework demonstrates superior data efficiency and learning rates compared to the current state of the art