- Title
- Physics Informed Intrinsic Rewards in Reinforcement Learning
- Creator
- Jiang, Jiazhou; Fu, Minyue; Chen, Zhiyong
- Relation
- 2022 Australian & New Zealand Control Conference (ANZCC). Proceedings of the 2022 Australian & New Zealand Control Conference (ANZCC) (Gold Coast, Queensland 24-25 November, 2022) p. 69-74
- Publisher Link
- http://dx.doi.org/10.1109/ANZCC56036.2022.9966956
- Publisher
- Institute of Electronics and Electrical Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2022
- Description
- Model-free algorithms in Reinforcement Learning (RL) are known to be a powerful learning tool and have performed well in solving complex issues. However, RL training results are often poor when the reward function is sparse or misleading in short term. In this paper, we propose a physics informed intrinsic reward function to assist the agent to overcome this difficulty. We evaluate the proposed intrinsic reward method on different types of actor-critic (AC) algorithms. The experimental results show noticeable improvement.
- Subject
- space vehicles; training; moon; reinforcement learning; physics; tuning
- Identifier
- http://hdl.handle.net/1959.13/1489514
- Identifier
- uon:52718
- Identifier
- ISBN:9781665498876
- Language
- eng
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