Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2510.23424 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911234860777472 |
|---|---|
| author | Khelifi, Elouanes Saki, Amir Faghihi, Usef |
| author_facet | Khelifi, Elouanes Saki, Amir Faghihi, Usef |
| contents | Deep Q Networks (DQN) have shown remarkable success in various reinforcement learning tasks. However, their reliance on associative learning often leads to the acquisition of spurious correlations, hindering their problem-solving capabilities. In this paper, we introduce a novel approach to integrate causal principles into DQNs, leveraging the PEACE (Probabilistic Easy vAriational Causal Effect) formula for estimating causal effects. By incorporating causal reasoning during training, our proposed framework enhances the DQN's understanding of the underlying causal structure of the environment, thereby mitigating the influence of confounding factors and spurious correlations. We demonstrate that integrating DQNs with causal capabilities significantly enhances their problem-solving capabilities without compromising performance. Experimental results on standard benchmark environments showcase that our approach outperforms conventional DQNs, highlighting the effectiveness of causal reasoning in reinforcement learning. Overall, our work presents a promising avenue for advancing the capabilities of deep reinforcement learning agents through principled causal inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23424 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal Deep Q Network Khelifi, Elouanes Saki, Amir Faghihi, Usef Artificial Intelligence Deep Q Networks (DQN) have shown remarkable success in various reinforcement learning tasks. However, their reliance on associative learning often leads to the acquisition of spurious correlations, hindering their problem-solving capabilities. In this paper, we introduce a novel approach to integrate causal principles into DQNs, leveraging the PEACE (Probabilistic Easy vAriational Causal Effect) formula for estimating causal effects. By incorporating causal reasoning during training, our proposed framework enhances the DQN's understanding of the underlying causal structure of the environment, thereby mitigating the influence of confounding factors and spurious correlations. We demonstrate that integrating DQNs with causal capabilities significantly enhances their problem-solving capabilities without compromising performance. Experimental results on standard benchmark environments showcase that our approach outperforms conventional DQNs, highlighting the effectiveness of causal reasoning in reinforcement learning. Overall, our work presents a promising avenue for advancing the capabilities of deep reinforcement learning agents through principled causal inference. |
| title | Causal Deep Q Network |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.23424 |