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..
Anticipatory Fictitious Play
Authors: Alex Cloud, Albert Wang, Wesley Kerr
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an extensive comparison of our algorithm with fictitious play, proving an optimal O(t 1) convergence rate for certain classes of games, demonstrating superior performance numerically across a variety of games, and concluding with experiments that extend these algorithms to the setting of deep multiagent reinforcement learning. |
| Researcher Affiliation | Industry | Alex Cloud , Albert Wang and Wesley Kerr Riot Games EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Neu PL-FP/AFP |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing code for the work described, nor does it provide a direct link to such source code. |
| Open Datasets | Yes | We use two environments, our own Tiny Fighter, and Running With Scissors, from [Vezhnevets et al., 2020]. [Vezhnevets et al., 2020] and [Liu et al., 2022] (Appendix B.1.) feature further discussion of the environment. |
| Dataset Splits | No | The paper describes the training process and evaluation metrics but does not provide specific details on dataset split percentages or counts for training, validation, and testing. |
| Hardware Specification | No | For reinforcement learning, we use the Asynchronous Proximal Policy Optimization (APPO) algorithm [Schulman et al., 2017], a distributed actor-critic RL algorithm, as implemented in RLLib [Moritz et al., 2018] with a single GPU learner. |
| Software Dependencies | No | For reinforcement learning, we use the Asynchronous Proximal Policy Optimization (APPO) algorithm [Schulman et al., 2017], a distributed actor-critic RL algorithm, as implemented in RLLib [Moritz et al., 2018] with a single GPU learner. |
| Experiment Setup | No | Hyperparameter settings are given in the supplementary material, Section E. |