Learning to Drive a Bicycle Using Reinforcement Learning and Shaping

Abstract

We present and solve a real-world problem of learning to drive a bicycle. We solve the problem by online reinforcement learning using the Sarsa()-algorithm. Then we solve the composite problem of learning to balance a bicycle and then drive to a goal. In our approach the reinforcement function is independent of the task the agent tries to learn to solve. 1 Introduction Here we consider the problem of learning to balance on a bicycle. Having done this we want to drive the bicycle to a goal. The second problem is not as straightforward as it may seem. The learning agent has to solve two problems at the same time: Balancing on the bicycle and driving to a specific place. Recently, ideas from behavioural psychology have been adapted by reinforcement learning to solve this type of problem. We will return to this in section 3. In reinforcement learning an agent interacts with an environment or a system. At each time step the agent receives information on the state of the system and chooses ...

Cite

Text

Randløv and Alstrøm. "Learning to Drive a Bicycle Using Reinforcement Learning and Shaping." International Conference on Machine Learning, 1998.

Markdown

[Randløv and Alstrøm. "Learning to Drive a Bicycle Using Reinforcement Learning and Shaping." International Conference on Machine Learning, 1998.](https://mlanthology.org/icml/1998/randlv1998icml-learning/)

BibTeX

@inproceedings{randlv1998icml-learning,
  title     = {{Learning to Drive a Bicycle Using Reinforcement Learning and Shaping}},
  author    = {Randløv, Jette and Alstrøm, Preben},
  booktitle = {International Conference on Machine Learning},
  year      = {1998},
  pages     = {463-471},
  url       = {https://mlanthology.org/icml/1998/randlv1998icml-learning/}
}