# PC3D: Zero-Shot Cooperation Across Variable Rosters via Personalized Context Distillation

arXiv: https://arxiv.org/abs/2605.10377 · HTML: https://mlsanity.com/paper/2605.10377/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multi-agent reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- Builds on 37 of its 48 references that are in the map.
- First posted on arXiv 11 May 2026.

## Placed in

- Deep reinforcement learning methods / [Multi-agent reinforcement learning](https://mlsanity.com/deeprl/multi-agent-reinforcement-learning/)

## Builds on (most cited first, 30 of 37)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?](https://mlsanity.com/paper/2011.09533/) (2020)
- [Emergence of Grounded Compositional Language in Multi-Agent Populations](https://mlsanity.com/paper/1703.04908/) (2017)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020)
- [Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks](https://mlsanity.com/paper/2006.07869/) (2020)
- [Understanding the impact of entropy on policy optimization](https://mlsanity.com/paper/1811.11214/) (2018)
- [A Review of Cooperative Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1908.03963/) (2019)
- [Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.07169/) (2020)
- [A Brief Review of Hypernetworks in Deep Learning](https://mlsanity.com/paper/2306.06955/) (2023)
- [An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2409.03052/) (2024)
- [Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.10423/) (2020)
- [Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics](https://mlsanity.com/paper/1910.01465/) (2019)
- [PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1911.00025/) (2019)
- [Learning Transferable Cooperative Behavior in Multi-Agent Teams](https://mlsanity.com/paper/1906.01202/) (2019)
- [A Survey of Progress on Cooperative Multi-agent Reinforcement Learning in Open Environment](https://mlsanity.com/paper/2312.01058/) (2023)
- [Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.04222/) (2020)
- [BenchMARL: Benchmarking Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2312.01472/) (2023)

## Abstract (arXiv)

Cooperative multi-agent reinforcement learning often assumes a fixed execution team, yet many decentralized systems must operate with varying numbers of active agents during deployment. We study this setting under episodic roster variation: each episode is executed by a set of homogeneous agents, with the team size varying across episodes. Agents act only from local histories, without execution-time communication, privileged coordinators, or online retraining. Therefore, effective cooperation requires each agent to recover relevant context about the active team and adapt its behavior accordingly. To this end, we propose PC3D (Personalized Central Coordination Context Distillation), a method for training decentralized policies to recover and use personalized coordination context from local interaction histories. During training, a set-structured centralized teacher compresses the active team into coordination tokens and personalizes them into agent-specific contexts, which are distilled into decentralized policies. At execution, each agent predicts its own context from local history and adaptively uses it to condition decision-making. Across three cooperative MARL benchmarks, PC3D achieves higher returns than the evaluated baselines with both seen and unseen roster sizes, and ablations attribute these gains to both context distillation and adaptive context use.