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2. Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning

   Carmona, Rene ; Lauriere, Mathieu ( October 2019 , ArXivorg)

   We develop a general reinforcement learning framework for mean field control (MFC) problems. Such problems arise for instance as the limit of collaborative multi-agent control problems when the number of agents is very large. The asymptotic problem can be phrased as the optimal control of a non-linear dynamics. This can also be viewed as a Markov decision process (MDP) but the key difference with the usual RL setup is that the dynamics and the reward now depend on the state's probability distribution itself. Alternatively, it can be recast as a MDP on the Wasserstein space of measures. In this work, we introduce generic model-free algorithms based on the state-action value function at the mean field level and we prove convergence for a prototypical Q-learning method. We then implement an actor-critic method and report numerical results on two archetypal problems: a finite space model motivated by a cyber security application and a continuous space model motivated by an application to swarm motion. 

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3. Field dislocation mechanics and phase field crystal models

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5. ELVIS: A Correlated Light-Field and Digital Holographic Microscope for Field and Laboratory Investigations – Field Demonstration

   https://doi.org/10.1017/S1551929520001133  

   Kim, Taewoo ; Serabyn, Eugene ; Liewer, Kurt ; Oborny, Nathan ; Wallace, J. Kent ; Rider, Stephanie ; Bedrossian, Manuel ; Lindensmith, Christian ; Nadeau, Jay ( July 2020 , Microscopy Today)

   Following the previous article, here we describe the first field demonstration of the ELVIS system, performed at Newport Beach, CA. We examined ocean water to detect microorganisms using the combined holographic and light-field fluorescence microscope and successfully detected both eukaryotes and prokaryotes. The shared field of view provided simultaneous bright-field (amplitude), phase, and fluorescence information from both chlorophyll autofluorescence and acridine orange staining. The entire process was performed in a nearly autonomous manner using a specifically designed sample processing unit (SPU) and custom acquisition software. We also discuss improvements to the system made after the field test that will make it more broadly useful to other types of fluorophores and samples. 

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