Reinforcement Learning on autonomous humanoid robots

Service robots have the potential to be of great value in households, health care and other labor intensive environments. However, these environments are typically unique, not very structured and frequently changing, which makes it difficult to make service robots robust and versatile through manual programming. Having robots learn to solve...

Accelerating reinforcement learning on a robot by using subgoals in a hierarchical framework

Reinforcement learning is a way to learn control tasks by trial and error. Even for simple motor control tasks, however, this can take a long time. We can speed up learning by using prior knowledge, but this is not always available, especially for an autonomous agent. One way to add limited prior knowledge is to use subgoals, defining points...

The Effects of Large Disturbances on On-Line Reinforcement Learning for aWalking Robot

Reinforcement Learning is a promising paradigm for adding learning capabilities to humanoid robots. One of the difficulties of the real world is the presence of disturbances. In Reinforcement Learning, disturbances are typically dealt with stochastically. However, large and infrequent disturbances do not fit well in this framework; essentially,...

The influence of motivational factors on choice behaviour

In this paper we investigate if and how motivational factors influence choice behaviour. We study four motivational factors: attitude towards car use, personal norm to reduce car use, car use habit, and perceived behavioural control to change car use to explain the choice behaviour of respondents in a stated choice experiment on the behavioural...