Incorporating advice into agents that learn from reinforcements
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Adaptive Behavior
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Imitation in animals and artifacts
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Tree-Based Batch Mode Reinforcement Learning
The Journal of Machine Learning Research
Efficient symmetry breaking for boolean satisfiability
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
| Hi-index | 0.00 |
In this paper, we apply Reinforcement Learning (RL) to a real-world task. While complex problems have been solved by RL in simulated worlds, the costs of obtaining enough training examples often prohibits the use of plain RL in real-world scenarios. We propose three approaches to reduce training expenses for real-world RL. Firstly, we replace the random exploration of the huge search space, which plain RL uses, by guided exploration that imitates a teacher. Secondly, we use experiences not only once but store and reuse them later on when their value is easier to assess. Finally, we utilize function approximators in order to represent the experience in a way that balances between generalization and discrimination. We evaluate the performance of the combined extensions of plain RL using a humanoid robot in the RoboCup soccer domain. As we show in simulation and real-world experiments, our approach enables the robot to quickly learn fundamental soccer skills.