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...

Speeding Up Reinforcement Learning with Graphics Processing Units

Conventionally programmed systems (e.g. robots) are not able to adapt to unforeseen changes in their task or environment. Reinforcement learning (RL), a machine learning approach, could grant this flexibility. Many fields of work could greatly benefit from this, be it in terms of cost, time or some other parameter. With RL, a learning agent...

Learning while preventing mechanical failure due to random motions

In this thesis one of the negative effects of learning from scratch on the durability of LEO is analysed. LEO is one of the bipedal walking robots of the TU Delft Robotics Institute. It uses Reinforcement learning to learn a stable and energy efficient walking gait. LEO’s learning algorithm causes its gears to fail faster during the initial...

Avoiding failure states during reinforcement learning

The Delft Biorobotics Laboratory develops bipedal humanoid robots. One of these robots, called LEO, is designed to learn to walk using reinforcement learning. During learning, LEO will make mistakes and fall. These mistakes can cause serious dam- age to the system but are an integral part of the learning process. A likely solution is punishing...