Abstract
In this paper, we propose a CNN-based inverse reinforcement learning method that optimizes a reward function modeled by a linear combination. The proposed method efficiently extracts features from expert demonstrations using a CNN-based network and effectively estimates the reward function with a few iterations. The proposed method is called CNN-based apprenticeship learning for inverse reinforcement learning. The policy estimated by this method guarantees performance similar to or better than that of expert behavior. Through the Super Mario simulation, we demonstrate that the proposed CNN-based apprenticeship learning outperforms traditional imitation learning and reinforcement learning methods.
| Original language | English |
|---|---|
| Title of host publication | 2024 24th International Conference on Control, Automation and Systems, ICCAS 2024 |
| Publisher | IEEE Computer Society |
| Pages | 73-78 |
| Number of pages | 6 |
| ISBN (Electronic) | 9788993215380 |
| DOIs | |
| State | Published - 2024 |
| Event | 24th International Conference on Control, Automation and Systems, ICCAS 2024 - Jeju, Korea, Republic of Duration: 29 Oct 2024 → 1 Nov 2024 |
Publication series
| Name | International Conference on Control, Automation and Systems |
|---|---|
| ISSN (Print) | 1598-7833 |
Conference
| Conference | 24th International Conference on Control, Automation and Systems, ICCAS 2024 |
|---|---|
| Country/Territory | Korea, Republic of |
| City | Jeju |
| Period | 29/10/24 → 1/11/24 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 4 Quality Education
Keywords
- Apprenticeship Learning via Inverse Reinforcement Learning (ALIRL)
- Convolutional Neural Network (CNN)
- Deep Q-Network (DQN)
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