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Reinforcement learning under high noise and uncertainty

Evaluation and improvement of reinforcement learning (RL) approaches to learn control strategies under challenging conditions

Reinforcement learning (RL) methods interact with a system in closed loop in order to learn suitable action strategies. The goal of this interaction is to control the system, e.g. to bring it into a desired state.

Interacting with the brain in closed loop is specifically challenging, as this "system" exhibits a few characteristics (noisy state estimates, history-dependent results of actions, non-stationarity) that are extremely challenging for standard RL algorithms.

Based on a simulation environment, which implements some of these challenging characteristics, standard RL algorithms and modified RL approaches shall be compared, in order to identify model classes which are best suited for follow-up closed-loop experiments in neurotechnological applications.

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