- 51 views
- 88 downloads
Reinforcement Teaching
-
- Author / Creator
- Muslimani, Calarina
-
While traditional machine learning algorithms learn to solve a task directly, meta- learning aims to learn about and improve another learning algorithm’s performance. However, existing meta-learning methods either only work with differentiable algo- rithms or are handcrafted to improve a specific component of an algorithm. There- fore, we develop a unifying meta-learning framework called reinforcement teaching to improve the learning process of any algorithm. Within the reinforcement teaching framework, a teaching policy is learned through reinforcement to improve a student’s learning. To effectively learn such a teaching policy, we develop a reward function based on learning progress, allowing the teacher’s policy to maximize the student’s performance more quickly. Further, we introduce a parametric-behavior embedder that learns a representation of the student’s learnable parameters from its input- output behavior. Finally, to demonstrate the effectiveness of reinforcement teaching, we perform a case study applying reinforcement teaching to the automatic curricu- lum learning domain. In this setting, a curriculum policy is learned that selects sub-tasks for a reinforcement learning student, outperforming handcrafted heuristics and previously proposed reward functions. To that end, reinforcement teaching is a framework capable of unifying different meta-learning approaches while effectively leveraging existing tools from reinforcement learning research.
-
- Subjects / Keywords
-
- Graduation date
- Fall 2022
-
- Type of Item
- Thesis
-
- Degree
- Master of Science
-
- License
- This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.