Reinforcement Learning for Flight Control of the Flying V

Recent research on the Flying V - a flying-wing long-range passenger aircraft - shows that its airframe design is 25% more aerodynamically efficient than a conventional tube-and-wing airframe. The Flying V is therefore a promising contribution towards reduction in climate impact of long-haul flights. However, some design aspects of the Flying V...

Toward Reliable Robot Navigation Using Deep Reinforcement Learning

Reliable indoor navigation in the presence of dynamic obstacles is an essential capability for mobile robot deployment. Previous work on robot navigation focuses on expanding the network structure and hardware setup leading to more complex and costly systems. The accompanying physical demonstrations are often limited to slow-moving agents and...

Controlling the estimation bias in deep reinforcement learning problems with sparse rewards: Towards robust robotic object manipulation learning

Many recent robot learning problems, real and simulated, were addressed using deep reinforcement learning. The developed policies can deal with high-dimensional, continuous state and action spaces, and can also incorporate machine-generated or human demonstration data. A great number of them depend on state-action value estimates, especially the...

Low level quadcopter control using Reinforcement Learning: Developing a self-learning drone

Reinforcement Learning (RL) is a learning paradigm where an agent learns a task by trial and error. The agent needs to explore its environment and by simultaneously receiving rewards it learns what is appropriate behaviour.<br/>Even though it has roots in machine learning, RL is essentially different from other machine learning methods. In...