arXiv Machine Learning August 7, 2026 By Conor M. Artman, Nicholas Di, Scott Perkins

IFlowNets: Extending Generative Samplers to Learn Strategies in Incomplete Information Games

Read the original on arXiv Machine Learning →

arXiv:2608. 05422v1 Announce Type: new Abstract: While many algorithms blend reinforcement learning (RL) with counterfactual regret (CFR) methods to leverage tradeoffs in computational speed and performance, there are fewer investigations into generative sampling frameworks in game theoretic applications in incomplete information games.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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