End-to-End Hierarchical Reinforcement Learning for Adaptive Flight Control: A method for model-independent control through Proximal Policy Optimization with learned Options

Aircraft with disruptive designs have no high-fidelity and accurate flight models. At the same time, developing models for stochastic phenomena for traditional aircraft configurations are costly, and classical control methods cannot operate beyond the predefined operation points or adapt to unexpected changes to the aircraft. The Proximal Policy...

Hierarchical Reinforcement Learning for Model-Free Flight Control: A sample efficient tabular approach using Q(lambda)-learning and options in a traditional flight control structure

Reinforcement learning (RL) is a model-free adaptive approach to learn a non-linear control law for flight control. However, for flat-RL (FRL) the size of the search space grows exponentially with the number of states, resulting in low sample efficiency. This research aims to improve the efficiency with Hierarchical Reinforcement Learning (HRL)....

An Empirical Approach to Reinforcement Learning for Micro Aerial Vehicles

The use of Micro Aerial Vehicles (MAVs) in practical applications, to solve real-world problems, is growing in demand as the technology becomes more widely known and accessible. Proposed applications already span a wide berth of fields like military, search and rescue, ecology, artificial pollinators, and more. As compared to larger Unmanned...

Hierarchical Reinforcement Learning for Spatio-temporal Planning

Reinforcement learning (RL) is an area of Machine Learning (ML) concerned with learning how a software-defined agent should act in an environment to maximize the rewards. Similar to many ML methods, RL suffers from the curse of dimensionality, the exponential increase in solution space with the increase in problem dimensions. Learning the...

Online reinforcement learning control for aerospace systems

Reinforcement Learning (RL) methods are relatively new in the field of aerospace guidance, navigation, and control. This dissertation aims to exploit RL methods to improve the autonomy and online learning of aerospace systems with respect to the a priori unknown system and environment, dynamical uncertainties, and partial observability. In the...

Safe Online Robust Exploration for Reinforcement Learning Control of Unmanned Aerial Vehicles