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This research introduces IFlowNets, which extends generative flow networks from complete information games to incomplete settings by correcting previous constraints on valid strategy densities. Preliminary experiments demonstrate that this new framework matches or exceeds the performance and speed of standard reinforcement learning methods like OSMCCFR in multi-agent environments. The work advances game-theoretic AI applications where agents must learn strategies under uncertainty without full observability.
IFlowNets: Extending Generative Samplers to Learn Strategies in Incomplete Information Games
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. We extend a generative flow network framework, Adversarial Flow Networks (AFlowNets), to incomplete information games, called Information Flow Networks (IFNs). We prove that previously established constraints for generative flow networks in complete information games are inadmissible for obtaining valid densities (corresponding to player strategies) and a valid training objective. We show that our proposed generalization, IFlowNets, alleviates this issue and strictly generalizes AFlowNets. In preliminary results for three standard game environments, IFlowNets perform comparably to or better than Outcome Sampling Monte Carlo Counterfactual Regret (OSMCCFR) and standard RL-based methods in performance and speed.
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. We extend a generative flow network framework, Adversarial Flow Networks (AFlowNets), to incomplete information games, called Information Flow Networks (IFNs). We prove that previously established constraints for generative flow networks in complete information games are inadmissible for obtaining valid densities (corresponding to player strategies) and a valid training objective. We show that our proposed generalization, IFlowNets, alleviates this issue and strictly generalizes AFlowNets. In preliminary results for three standard game environments, IFlowNets perform comparably to or better than Outcome Sampling Monte Carlo Counterfactual Regret (OSMCCFR) and standard RL-based methods in performance and speed.
Machine Learning (cs.LG); Multiagent Systems (cs.MA)
arXiv:2608.05422 [cs.LG]
(or arXiv:2608.05422v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.05422 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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