Spiking Neural-Networks-Based Data-Driven Control

Machine learning can be effectively applied in control loops to make optimal control decisions robustly. There is increasing interest in using spiking neural networks (SNNs) as the apparatus for machine learning in control engineering because SNNs can potentially offer high energy efficiency, and new SNN-enabling neuromorphic hardware is being...

Evolving-to-Learn with Spiking Neural Networks

Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks. Different from learning algorithms such as propagation and evolution that are widely used to train spiking neural networks, synaptic plasticity rules learn the parameters with local information, making them suitable for online learning...

Secondary Frequency Control of Microgrids: An Online Reinforcement Learning Approach

In this article, we present a reinforcement learning-based scheme for secondary frequency control of lossy inverter-based microgrids. Compared with the existing methods in the literature, we relax the common restrictions on the system, i.e., being lossless, and the transmission lines and loads to have known constant impedances. The proposed...

Flowsheet generation through hierarchical reinforcement learning and graph neural networks

Process synthesis experiences a disruptive transformation accelerated by artificial intelligence. We propose a reinforcement learning algorithm for chemical process design based on a state-of-the-art actor-critic logic. Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from...

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...

Reinforcement Learning of Visual Features

The digital environment has an ever increasing amount smart programs. Programs that also get smarter every day. They help us filtering spam e-mail and they adjust to show us personalized advertisements. These smart programs observe people and serve (other) people. A robot can be seen as a program with a body. Make the program smart enough and it...

Consolidated Deep Actor Critic Networks (DRAFT)

The works [Volodymyr et al. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.] and [Volodymyr et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015.] have demonstrated the power of combining deep neural networks with Watkins Q learning. They introduce deep Q networks ...

Nonlinear System Identification and Control for Autonomous Robots

Most robots receive information about their own dynamics or about their environment before starting to work on their tasks. This makes them less fit for operating in unknown environments without human supervision. In this thesis, the goal is to let a robot learn the relation between its actions and perceptions autonomously. This problem is...