In terms of papers using relevant tasks, the closest example I can think of might be the "hide and seek" paradigm used by Weihs et al. (2019), which I include in my list below (not my area of expertise - so there could easily be far more relevant papers out there that I'm not aware of). I wouldn't be the least bit surprised though if there were lots of relevant ideas that you could take from prior works using symmetric games as well, so I've also included a wide variety of other papers that all fall under the broad umbrella of modeling game-learning/game-behavior.
Guennouni, I., & Speekenbrink, M. (n.d.). Transfer of Learned Opponent Models in Zero Sum Games. 8. Hawkins, R. D., Frank, M. C., & Goodman, N. D. (2020). Characterizing the dynamics of learning in repeated reference games. Cognitive Science, 44(6), e12845. http://arxiv.org/abs/1912.07199
Lampinen, A. K., & McClelland, J. L. (2020). Transforming task representations to perform novel tasks. Proceedings of the National Academy of Sciences, 117(52), 32970–32981. https://doi.org/10.1073/pnas.2008852117
Lensberg, T., & Schenk-Hoppé, K. R. (2021). Cold play: Learning across bimatrix games. Journal of Economic Behavior & Organization, 185, 419–441. https://doi.org/10.1016/j.jebo.2021.02.027
Schwarzer, M., Rajkumar, N., Noukhovitch, M., Anand, A., Charlin, L., Hjelm, D., Bachman, P., & Courville, A. (2021). Pretraining Representations for Data-Efficient Reinforcement Learning. http://arxiv.org/abs/2106.04799
Sibert, C., Gray, W. D., & Lindstedt, J. K. (2017). Interrogating Feature Learning Models to Discover Insights Into the Development of Human Expertise in a Real-Time, Dynamic Decision-Making Task. Topics in Cognitive Science, 9(2), 374–394. https://doi.org/10.1111/tops.12225
Spiliopoulos, L. (2013). Beyond fictitious play beliefs: Incorporating pattern recognition and similarity matching. Games and Economic Behavior, 81, 69–85. https://doi.org/10.1016/j.geb.2013.04.005
Spiliopoulos, L. (2015). Transfer of conflict and cooperation from experienced games to new games: A connectionist model of learning. Frontiers in Neuroscience, 9. https://doi.org/10.3389/fnins.2015.00102
Stanić, A., Tang, Y., Ha, D., & Schmidhuber, J. (2022). Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (No. arXiv:2208.03374). arXiv. http://arxiv.org/abs/2208.03374
Tsividis, P. A., Loula, J., Burga, J., Foss, N., Campero, A., Pouncy, T., Gershman, S. J., & Tenenbaum, J. B. (2021). Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning. http://arxiv.org/abs/2107.12544
Weihs, L., Kembhavi, A., Han, W., Herrasti, A., Kolve, E., Schwenk, D., Mottaghi, R., & Farhadi, A. (2019). Artificial Agents Learn Flexible Visual Representations by Playing a Hiding Game.http://arxiv.org/abs/1912.08195
Zheng, Z. (Sam)., Lin, X. (Daisy)., Topping, J., & Ma, W. J. (2022). Comparing Machine and Human Learning in a Planning Task of Intermediate Complexity. Proceedings of the Annual Meeting of the Cognitive Science Society, 44(44). https://escholarship.org/uc/item/8wm748d8
InfuriatinglyOpaque t1_ivb9otw wrote
Reply to Training a board game player AI for an asymmetric game by computing_professor
In terms of papers using relevant tasks, the closest example I can think of might be the "hide and seek" paradigm used by Weihs et al. (2019), which I include in my list below (not my area of expertise - so there could easily be far more relevant papers out there that I'm not aware of). I wouldn't be the least bit surprised though if there were lots of relevant ideas that you could take from prior works using symmetric games as well, so I've also included a wide variety of other papers that all fall under the broad umbrella of modeling game-learning/game-behavior.
References
Aggarwal, P., & Dutt, V. (2020). Role of information about opponent’s actions and intrusion-detection alerts on cyber-decisions in cybersecurity games. Cyber Security: https://www.ingentaconnect.com/content/hsp/jcs/2020/00000003/00000004/art00008
Amiranashvili, A., Dorka, N., Burgard, W., Koltun, V., & Brox, T. (2020). Scaling Imitation Learning in Minecraft. http://arxiv.org/abs/2007.02701
Bramlage, L., & Cortese, A. (2021). Generalized Attention-Weighted Reinforcement Learning. Neural Networks. https://doi.org/10.1016/j.neunet.2021.09.023
Frey, S., & Goldstone, R. L. (2013). Cyclic Game Dynamics Driven by Iterated Reasoning. PLoS ONE, 8(2), e56416. https://doi.org/10.1371/journal.pone.0056416
Guennouni, I., & Speekenbrink, M. (n.d.). Transfer of Learned Opponent Models in Zero Sum Games. 8. Hawkins, R. D., Frank, M. C., & Goodman, N. D. (2020). Characterizing the dynamics of learning in repeated reference games. Cognitive Science, 44(6), e12845. http://arxiv.org/abs/1912.07199
Kumaran, V., Mott, B. W., & Lester, J. C. (2019.). Generating Game Levels for Multiple Distinct Games with a Common Latent Space. 7. https://ojs.aaai.org/index.php/AIIDE/article/view/7418
Lampinen, A. K., & McClelland, J. L. (2020). Transforming task representations to perform novel tasks. Proceedings of the National Academy of Sciences, 117(52), 32970–32981. https://doi.org/10.1073/pnas.2008852117
Lensberg, T., & Schenk-Hoppé, K. R. (2021). Cold play: Learning across bimatrix games. Journal of Economic Behavior & Organization, 185, 419–441. https://doi.org/10.1016/j.jebo.2021.02.027
Schwarzer, M., Rajkumar, N., Noukhovitch, M., Anand, A., Charlin, L., Hjelm, D., Bachman, P., & Courville, A. (2021). Pretraining Representations for Data-Efficient Reinforcement Learning. http://arxiv.org/abs/2106.04799
Sibert, C., Gray, W. D., & Lindstedt, J. K. (2017). Interrogating Feature Learning Models to Discover Insights Into the Development of Human Expertise in a Real-Time, Dynamic Decision-Making Task. Topics in Cognitive Science, 9(2), 374–394. https://doi.org/10.1111/tops.12225
Spiliopoulos, L. (2013). Beyond fictitious play beliefs: Incorporating pattern recognition and similarity matching. Games and Economic Behavior, 81, 69–85. https://doi.org/10.1016/j.geb.2013.04.005
Spiliopoulos, L. (2015). Transfer of conflict and cooperation from experienced games to new games: A connectionist model of learning. Frontiers in Neuroscience, 9. https://doi.org/10.3389/fnins.2015.00102
Stanić, A., Tang, Y., Ha, D., & Schmidhuber, J. (2022). Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter (No. arXiv:2208.03374). arXiv. http://arxiv.org/abs/2208.03374
Tsividis, P. A., Loula, J., Burga, J., Foss, N., Campero, A., Pouncy, T., Gershman, S. J., & Tenenbaum, J. B. (2021). Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning. http://arxiv.org/abs/2107.12544
Weihs, L., Kembhavi, A., Han, W., Herrasti, A., Kolve, E., Schwenk, D., Mottaghi, R., & Farhadi, A. (2019). Artificial Agents Learn Flexible Visual Representations by Playing a Hiding Game. http://arxiv.org/abs/1912.08195
Zheng, Z. (Sam)., Lin, X. (Daisy)., Topping, J., & Ma, W. J. (2022). Comparing Machine and Human Learning in a Planning Task of Intermediate Complexity. Proceedings of the Annual Meeting of the Cognitive Science Society, 44(44). https://escholarship.org/uc/item/8wm748d8