Stepping Out of the Shadows: Reinforcement Learning in Shadow Mode

TL;DR

This paper introduces a shadow mode reinforcement learning approach that trains agents alongside conventional controllers to improve performance in cyber-physical systems while minimizing risks and training time.

Contribution

The novel shadow mode training method allows RL agents to learn from existing controllers, reducing risk and training time in physical systems with no simulation models.

Findings

Effective training on reach-avoid task where standard methods fail

Low regret during training by combining RL and conventional control

Improved system performance over traditional controllers

Abstract

Reinforcement learning (RL) is not yet competitive for many cyber-physical systems, such as robotics, process automation, and power systems, as training on a system with physical components cannot be accelerated, and simulation models do not exist or suffer from a large simulation-to-reality gap. During the long training time, expensive equipment cannot be used and might even be damaged due to inappropriate actions of the reinforcement learning agent. Our novel approach addresses exactly this problem: We train the reinforcement agent in a so-called shadow mode with the assistance of an existing conventional controller, which does not have to be trained and instantaneously performs reasonably well. In shadow mode, the agent relies on the controller to provide action samples and guidance towards favourable states to learn the task, while simultaneously estimating for which states the learned agent will receive a higher reward than the conventional controller. The RL agent will then control the system for these states and all other regions remain under the control of the existing controller. Over time, the RL agent will take over for an increasing amount of states, while leaving control to the baseline, where it cannot surpass its performance. Thus, we keep regret during training low and improve the performance compared to only using conventional controllers or reinforcement learning. We present and evaluate two mechanisms for deciding whether to use the RL agent or the conventional controller. The usefulness of our approach is demonstrated for a reach-avoid task, for which we are able to effectively train an agent, where standard approaches fail.