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Martin Bichler

3 accepted papers

2026

Deep Reinforcement Learning Finds Bayes-Nash Equilibrium in Competitive Newsvendor Problems

ICML 2026poster

We investigate learning dynamics in competitive newsvendor games, a class of continuous-action games with strategic substitutes. Despite established equilibrium properties, convergence of independent learning algorithms in repeated general-sum play remains uncertain. We analyze structural properties…

Cited by 0SourceScholar
2025

Beyond Monotonicity: On the Convergence of Learning Algorithms in Standard Auction Games

AAAI 2025technical

Equilibrium problems in Bayesian auction games can be described as systems of differential equations. Depending on the model assumptions, these equations might be such that we do not have a rigorous mathematical solution theory. The lack of analytical or numerical techniques with guaranteed converge…

Cited by 0SourcePDFScholar
2023

Enabling First-Order Gradient-Based Learning for Equilibrium Computation in Markets

ICML 2023poster

Understanding and analyzing markets is crucial, yet analytical equilibrium solutions remain largely infeasible. Recent breakthroughs in equilibrium computation rely on zeroth-order policy gradient estimation. These approaches commonly suffer from high variance and are computationally expensive. The…