Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Joint Policy Search for Multi-agent Collaboration with Imperfect Information
Authors: Yuandong Tian, Qucheng Gong, Yu Jiang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On multi-agent collaborative tabular games, JPS is proven to never worsen performance and can improve solutions provided by unilateral approaches (e.g., CFR [44]), outperforming algorithms designed for collaborative policy learning (e.g. BAD [16]). Furthermore, for real-world game with exponential states, JPS has an online form that naturally links with gradient updates. We test it to Contract Bridge, a 4-player imperfect-information game where a team of 2 collaborates to compete against the other. |
| Researcher Affiliation | Industry | Yuandong Tian Facebook AI Research EMAIL Qucheng Gong Facebook AI Research EMAIL Tina Jiang Facebook AI Research EMAIL |
| Pseudocode | Yes | Algorithm 1 Joint Policy Search (Tabular form) |
| Open Source Code | Yes | Part of the code is released in https://github.com/facebookresearch/jps. |
| Open Datasets | Yes | We generate a training set of 2.5 million hands, drawn from uniform distribution on permutations of 52 cards. We pre-compute their DDS results. The evaluation dataset contains 50k such hands. Both datasets will be open sourced for the community and future work. |
| Dataset Splits | No | The paper mentions a "training set of 2.5 million hands" and an "evaluation dataset contains 50k such hands" but does not explicitly specify a validation set or clear training/testing/validation split percentages or sample counts for these datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as CPU or GPU models, or cloud computing specifications. It mentions "massive computational resources" in general terms. |
| Software Dependencies | No | The paper mentions software like A2C, CFR, and BAD, but does not specify version numbers for any of the software dependencies or libraries used in their implementation. |
| Experiment Setup | Yes | During training we run 2000 games in parallel, use batch size of 1024, an entropy ratio of 0.01 and with no discount factor. ... JPS uses a search depth of D = 3 ... After the P1 s turn, we rollout 5 times to sample opponent s actions under Ī. After P2 s turn, we rollout 5 times following Ī to get an estimate of vĪ(h). Therefore, for each initial state h0, we run 5 Ã 5 rollouts for each combination of policy candidates of P1 and P2. Only a small fraction (e.g., 5%) of the games stopped at some game state and run the search procedure above. |