Fetching the paper…
Reading the bibliography…
Robustly cooperating with unseen agents and human partners presents significant challenges due to the diverse cooperative conventions these partners may adopt.
Ad hoc autonomous agent teams: Collaboration without pre-coordination
Stone, P.; Kaminka, G.; Kraus, S.; and Rosenschein, J. 2010 · 2010
Earlier work this paper cites.
Convergence, targeted optimality and safety in multiagent learning
Chakraborty, D.; and Stone, P. 2014 · 2014
Earlier work this paper cites.
Making Friends on the Fly: Cooperating with New Teammates
Barrett, S.; Rosenfeld, A.; Kraus, S.; and Stone, P. 2016 · 2016
Earlier work this paper cites.
RL 2 \text{RL}^{2} : Fast reinforcement learning via slow reinforcement learning
Duan, Y.; Schulman, J.; Chen, X.; Bartlett, P. L.; Sutskever, I.; and Abbeel, P. 2016 · 2016
Earlier work this paper cites.
Maven: Multi-agent variational exploration
Mahajan, A.; Rashid, T.; Samvelyan, M.; and Whiteson, S. 2019 · 2019
Earlier work this paper cites.
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Vinyals, O.; Babuschkin, I.; Czarnecki, W. M.; Mathieu, M.; Dudzik, A.; Chung, J.; Choi, D. H.; Powell, R.; Ewalds, T.; Georgiev, P.; et al. 2019 · 2019
Earlier work this paper cites.
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Christianos, F.; Schäfer, L.; and Albrecht, S. V. 2020 · 2020
Earlier work this paper cites.
“other-play” for zero-shot coordination
Hu, H.; Lerer, A.; Peysakhovich, A.; and Foerster, J. 2020 · 2020
Cited alongside, same era.
Online ad hoc teamwork under partial observability
Gu, P.; Zhao, M.; Hao, J.; and An, B. 2021 · 2021
Cited alongside, same era.
Trajectory diversity for zero-shot coordination
Lupu, A.; Cui, B.; Hu, H.; and Foerster, J. 2021 · 2021
Cited alongside, same era.
Agent Modelling under Partial Observability for Deep Reinforcement Learning
Papoudakis, G.; Christianos, F.; and Albrecht, S. 2021 · 2021
Cited alongside, same era.
Towards Open Ad Hoc Teamwork Using Graph-Based Policy Learning
Rahman, A.; Höpner, N.; Christianos, F.; and Albrecht, S. V. 2021 · 2021
Cited alongside, same era.
Collaborating with humans without human data
Strouse, D.; McKee, K.; Botvinick, M.; Hughes, E.; and Everett, R. 2021 · 2021
Cited alongside, same era.
Deep interactive bayesian reinforcement learning via meta-learning
Zintgraf, L.; Devlin, S.; Ciosek, K.; Whiteson, S.; and Hofmann, K. 2021 · 2021
Later among the works it cites.
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning
Bakhtin, A.; Wu, D. J.; Lerer, A.; Gray, J.; Jacob, A. P.; Farina, G.; Miller, A. H.; and Brown, N. 2022 · 2022
Later among the works it cites.
A survey of ad hoc teamwork research
Mirsky, R.; Carlucho, I.; Rahman, A.; Fosong, E.; Macke, W.; Sridharan, M.; Stone, P.; and Albrecht, S. V. 2022 · 2022
Later among the works it cites.
The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games
Yu, C.; Velu, A.; Vinitsky, E.; Gao, J.; Wang, Y.; Bayen, A.; and Wu, Y. 2022 · 2022
Later among the works it cites.
Generating Diverse Cooperative Agents by Learning Incompatible Policies
Charakorn, R.; Manoonpong, P.; and Dilokthanakul, N. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning with Generated Teammates to Achieve Type-Free Ad-Hoc Teamwork
Xing, D.; Liu, Q.; Zheng, Q.; Pan, G.; and Zhou, Z. 2021 · 2021
Cited alongside, same era.
Adversarial Diversity in Hanabi
Cui, B.; Lupu, A.; Sokota, S.; Hu, H.; Wu, D. J.; and Foerster, J. N. 2023a
Cited in the paper.
MACTA: A Multi-agent Reinforcement Learning Approach for Cache Timing Attacks and Detection
Cui, J.; Yang, X.; Luo, M.; Lee, G.; Stone, P.; Lee, H.-H. S.; Lee, B.; Suh, G. E.; Xiong, W.; and Tian, Y. 2023b
Cited in the paper.
Closest in time.
Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity
Rahman, A.; Fosong, E.; Carlucho, I.; and Albrecht, S. V. 2023 · 2023
Closest in time.