Approximating Auction Equilibria with Reinforcement Learning
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909353981771776 |
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| author | Rawat, Pranjal |
| author_facet | Rawat, Pranjal |
| contents | Traditional methods for computing equilibria in auctions become computationally intractable as auction complexity increases, particularly in multi-item and dynamic auctions. This paper introduces a self-play based reinforcement learning approach that employs advanced algorithms such as Proximal Policy Optimization and Neural Fictitious Self-Play to approximate Bayes-Nash equilibria. This framework allows for continuous action spaces, high-dimensional information states, and delayed payoffs. Through self-play, these algorithms can learn robust and near-optimal bidding strategies in auctions with known equilibria, including those with symmetric and asymmetric valuations, private and interdependent values, and multi-round auctions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13960 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Approximating Auction Equilibria with Reinforcement Learning Rawat, Pranjal General Economics Economics Artificial Intelligence Traditional methods for computing equilibria in auctions become computationally intractable as auction complexity increases, particularly in multi-item and dynamic auctions. This paper introduces a self-play based reinforcement learning approach that employs advanced algorithms such as Proximal Policy Optimization and Neural Fictitious Self-Play to approximate Bayes-Nash equilibria. This framework allows for continuous action spaces, high-dimensional information states, and delayed payoffs. Through self-play, these algorithms can learn robust and near-optimal bidding strategies in auctions with known equilibria, including those with symmetric and asymmetric valuations, private and interdependent values, and multi-round auctions. |
| title | Approximating Auction Equilibria with Reinforcement Learning |
| topic | General Economics Economics Artificial Intelligence |
| url | https://arxiv.org/abs/2410.13960 |