Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2024 IEEE Conference on Games, CoG 2024 |
| Publisher | IEEE Computer Society |
| ISBN (electronic) | 9798350350678 |
| ISBN (print) | 979-8-3503-5068-5 |
| Publication status | Published - 5 Aug 2024 |
| Event | 6th Annual IEEE Conference on Games, CoG 2024 - Milan, Italy Duration: 5 Aug 2024 → 8 Aug 2024 |
Publication series
| Name | IEEE Conference on Computatonal Intelligence and Games, CIG |
|---|---|
| ISSN (Print) | 2325-4270 |
| ISSN (electronic) | 2325-4289 |
Abstract
We propose RHEA CL, which combines Curriculum Learning (CL) with Rolling Horizon Evolutionary Algorithms (RHEA) to automatically produce effective curricula during the training of a reinforcement learning agent. RHEA CL optimizes a population of curricula, using an evolutionary algorithm, and selects the best-performing curriculum as the starting point for the next training epoch. Performance evaluations are conducted after every curriculum step in all environments. We evaluate the algorithm on the DoorKey and DynamicObstacles environments within the Minigrid framework. It demonstrates adaptability and consistent improvement, particularly in the early stages, while reaching a stable performance later that is capable of outperforming other curriculum learners. In comparison to other curriculum schedules, RHEA CL has shown to yield performance improvements for the final Reinforcement learning (RL) agent at the cost of additional evaluation during training.
Keywords
- Curriculum Learning, Evolutionary Algorithms, Reinforcement Learning, Rolling Horizon Algorithms
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Software
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Proceedings of the 2024 IEEE Conference on Games, CoG 2024. IEEE Computer Society, 2024. (IEEE Conference on Computatonal Intelligence and Games, CIG).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Online Optimization of Curriculum Learning Schedules using Evolutionary Optimization
AU - Jiwatode, Mohit
AU - Schlecht, Leon
AU - Dockhorn, Alexander
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024/8/5
Y1 - 2024/8/5
N2 - We propose RHEA CL, which combines Curriculum Learning (CL) with Rolling Horizon Evolutionary Algorithms (RHEA) to automatically produce effective curricula during the training of a reinforcement learning agent. RHEA CL optimizes a population of curricula, using an evolutionary algorithm, and selects the best-performing curriculum as the starting point for the next training epoch. Performance evaluations are conducted after every curriculum step in all environments. We evaluate the algorithm on the DoorKey and DynamicObstacles environments within the Minigrid framework. It demonstrates adaptability and consistent improvement, particularly in the early stages, while reaching a stable performance later that is capable of outperforming other curriculum learners. In comparison to other curriculum schedules, RHEA CL has shown to yield performance improvements for the final Reinforcement learning (RL) agent at the cost of additional evaluation during training.
AB - We propose RHEA CL, which combines Curriculum Learning (CL) with Rolling Horizon Evolutionary Algorithms (RHEA) to automatically produce effective curricula during the training of a reinforcement learning agent. RHEA CL optimizes a population of curricula, using an evolutionary algorithm, and selects the best-performing curriculum as the starting point for the next training epoch. Performance evaluations are conducted after every curriculum step in all environments. We evaluate the algorithm on the DoorKey and DynamicObstacles environments within the Minigrid framework. It demonstrates adaptability and consistent improvement, particularly in the early stages, while reaching a stable performance later that is capable of outperforming other curriculum learners. In comparison to other curriculum schedules, RHEA CL has shown to yield performance improvements for the final Reinforcement learning (RL) agent at the cost of additional evaluation during training.
KW - Curriculum Learning
KW - Evolutionary Algorithms
KW - Reinforcement Learning
KW - Rolling Horizon Algorithms
UR - http://www.scopus.com/inward/record.url?scp=85203509151&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2408.06068
DO - 10.48550/arXiv.2408.06068
M3 - Conference contribution
AN - SCOPUS:85203509151
SN - 979-8-3503-5068-5
T3 - IEEE Conference on Computatonal Intelligence and Games, CIG
BT - Proceedings of the 2024 IEEE Conference on Games, CoG 2024
PB - IEEE Computer Society
T2 - 6th Annual IEEE Conference on Games, CoG 2024
Y2 - 5 August 2024 through 8 August 2024
ER -