{"ID":2848327,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.25080","arxiv_id":"2510.25080","title":"Monopoly Deal: A Benchmark Environment for Bounded One-Sided Response Games","abstract":"Card games are widely used to study sequential decision-making under uncertainty, with real-world analogues in negotiation, finance, and cybersecurity. These games typically fall into three categories based on the flow of control: strictly sequential (players alternate single actions), deterministic response (some actions trigger a fixed outcome), and unbounded reciprocal response (alternating counterplays are permitted). A less-explored but strategically rich structure is the bounded one-sided response, where a player's action briefly transfers control to the opponent, who must satisfy a fixed condition through one or more moves before the turn resolves. We term games featuring this mechanism Bounded One-Sided Response Games (BORGs). We introduce a modified version of Monopoly Deal as a benchmark environment that isolates this dynamic, where a Rent action forces the opponent to choose payment assets. The gold-standard algorithm, Counterfactual Regret Minimization (CFR), converges on effective strategies without novel algorithmic extensions. A lightweight full-stack research platform unifies the environment, a parallelized CFR runtime, and a human-playable web interface. The trained CFR agent and source code are available at https://monopolydeal.ai.","short_abstract":"Card games are widely used to study sequential decision-making under uncertainty, with real-world analogues in negotiation, finance, and cybersecurity. These games typically fall into three categories based on the flow of control: strictly sequential (players alternate single actions), deterministic response (some acti...","url_abs":"https://arxiv.org/abs/2510.25080","url_pdf":"https://arxiv.org/pdf/2510.25080v2","authors":"[\"Will Wolf\"]","published":"2025-10-29T01:38:19Z","proceeding":"cs.GT","tasks":"[\"cs.GT\",\"cs.AI\",\"cs.LG\"]","methods":"[]","project_urls":"[\"https://monopolydeal.ai\"]","has_code":false}