{"paper":{"title":"Counter-Factual Reinforcement Learning: How to Model Decision-Makers That Anticipate The Future","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GT"],"primary_cat":"cs.MA","authors_text":"Brendan Tracey, David H. Wolpert, James Bono, Ritchie Lee, Russell Bent, Scott Backhaus","submitted_at":"2012-07-03T22:30:34Z","abstract_excerpt":"This paper introduces a novel framework for modeling interacting humans in a multi-stage game. This \"iterated semi network-form game\" framework has the following desirable characteristics: (1) Bounded rational players, (2) strategic players (i.e., players account for one another's reward functions when predicting one another's behavior), and (3) computational tractability even on real-world systems. We achieve these benefits by combining concepts from game theory and reinforcement learning. To be precise, we extend the bounded rational \"level-K reasoning\" model to apply to games over multiple "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1207.0852","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}