Quantum gradient estimation and its application to quantum reinforcement learning

In 2005, Jordan showed how to estimate the gradient of a real-valued function with a high-dimensional domain on a quantum computer. Subsequently, in 2017, it was shown by Gilyén et al. how to do this with a different input model. They also proved optimality of their algorithm for \ell^\infty -approximations of functions satisfying some...

Deep Reinforcement Learning for Goal-directed Visual Navigation

Safe navigation in a cluttered environment is a key capability for the autonomous operation of Micro Aerial Vehicles (MAVs). This work explores a (deep) Reinforcement Learning (RL) based approach for monocular vision based obstacle avoidance and goal directed navigation for MAVs in cluttered environments. We investigated this problem in the...

Adaptive Reinforcement Learning: Increasing the applicability for large and time varying systems using parallel Gaussian Process regression and adaptive nonlinear control

This thesis investigates the applicability of the Probabilistic Inference for Learning COntrol (PILCO) algorithm to large systems and systems with time varying measurement noise. PILCO is a state-of-the-art model-learning Reinforcement Learning (RL) algorithm that uses a Gaussian Process (GP) model to average over uncertainties during learning....

Mapping Elements of Reinforcement Learning to Human Emotions

Considerable overlap exists between emotion and Reinforcement Learning (RL). Emotion influences action selection while RL selects actions based on their anticipated result. Emotions also provide feedback on a situation, reflecting if the situation is desirable or not. The same type of feedback is given in RL based on the results of a state...