Dresden 2017 – scientific programme

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CPP: Fachverband Chemische Physik und Polymerphysik

CPP 44: Aktive Matter I (joint session DY/BP/CPP, organized by DY)

CPP 44.9: Talk

Wednesday, March 22, 2017, 17:15–17:30, HÜL 186

Reinforcement learning of artificial microswimmers — •Santiago Muiños-Landin¹, Keyan Ghazi-Zahedi², and Frank Cichos¹ — ¹Molecular Nanophotonics. University of Leipzig. Institut for Experimental Physics I — ²Information Theory of Cognitive Systems, Max Planck Institute for Mathematics in the Sciences

Reinforcement Learning (RL) is a special area of the Machine Learning discipline which consist in the search of an optimal policy in the context of Markovian Decision Processes (MDP). Learning is based on the interaction of the system with its environment and is guided by sparse rewards. In RL a policy is a function that connects the available actions that an agent can execute with the states where this agent can be located at. MDPs were already proposed as a model for the navigation of natural microswimmers. Here we present now a method that uses this RL in order to achieve an autonomous explorative behavior from a self-thermophoretic microswimmer. We implement it experimentally by photon nudging to reach reinforcement learning of a symmetric microswimmer.