Reinforcement Learning with Adaptive Control Regularization for Safe Control of Critical Systems

Reinforcement Learning (RL) is a powerful method for controlling dynamic systems, but its learning mechanism can lead to unpredictable actions that undermine the safety of critical systems. Here, we propose RL with Adaptive Control Regularization (RL-ACR) that ensures RL safety by combining the RL policy with a control regularizer that hard-codes safety constraints over forecasted system behaviors. The adaptability is achieved by using a learnable "focus" weight trained to maximize the cumulative reward of the policy combination. As the RL policy improves through off-policy learning, the focus weight improves the initial sub-optimum strategy by gradually relying more on the RL policy. We demonstrate the effectiveness of RL-ACR in a critical medical control application and further investigate its performance in four classic control environments.

Single-cell phase-contrast tomograms data encoded by 3D Zernike descriptors

Phase-contrast tomographic flow cytometry combines quantitative 3D analysis of unstained single cells and high-throughput. A crucial issue of this method is the storage and management of the huge amount of 3D tomographic data. Here we show an effective quasi lossless compression of tomograms data through 3D Zernike descriptors, unlocking data management tasks and computational pipelines that were unattainable until now.

Driving Reinforcement Learning with Models

Over the years, Reinforcement Learning (RL) established itself as a convenient paradigm to learn optimal policies from data. However, most RL algorithms achieve optimal policies by exploring all the possible actions and this, in real-world scenarios, is often infeasible or impractical due to e.g. safety constraints. Motivated by this, in this paper we propose to augment RL with Model Predictive Control (MPC), a popular model-based control algorithm that allows to optimally control a system while satisfying a set of constraints. The result is an algorithm, the MPC-augmented RL algorithm (MPCaRL) that makes use of MPC to both drive how RL explores the actions and to modify the corresponding rewards. We demonstrate the effectiveness of the MPCaRL by letting it play against the Atari game Pong. The results obtained highlight the ability of the algorithm to learn general tasks with essentially no training.