# PLATO: Pointer Learner for Agent and Task Openness

arXiv: https://arxiv.org/abs/2607.25082 · HTML: https://mlsanity.com/paper/2607.25082/

## 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 21 of its 38 references that are in the map.
- First posted on arXiv 27 Jul 2026.

## Placed in

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

## Builds on (most cited first, 21 of 21)

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- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
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- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2006.11438/) (2020)
- [Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning](https://mlsanity.com/paper/2006.10412/) (2020)
- [Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systems](https://mlsanity.com/paper/1911.08642/) (2019)
- [Multi-task Learning and Catastrophic Forgetting in Continual Reinforcement Learning](https://mlsanity.com/paper/1909.10008/) (2019)
- [Computing Maximum Matchings in Temporal Graphs](https://mlsanity.com/paper/1905.05304/) (2019)
- [Inaugural MOASEI Competition at AAMAS'2025: A Technical Report](https://mlsanity.com/paper/2507.05469/) (2025)
- [Challenges in Credit Assignment for Multi-Agent Reinforcement Learning in Open Agent Systems](https://mlsanity.com/paper/2510.27659/) (2025)
- [Modularity and Openness in Modeling Multi-Agent Systems](https://mlsanity.com/paper/1307.4477/) (2013)

## Abstract (arXiv)

Open agent systems (OASYS) are increasingly prevalent in real-world domains where the sets of agents and tasks change unpredictably over time. Such openness, including agent openness (AO) and task openness (TO), poses a fundamental challenge to multi-agent reinforcement learning (MARL), which typically assumes fixed state and action spaces. Existing methods address openness only partially: padding and masking approaches introduce artificial bounds, while recent graph-based or hypergraph methods handle one dimension of openness but still depend on restrictive assumptions. In this paper, we introduce Pointer Learner for Agent and Task Openness (PLATO), a pointer-network-based actor combined with a centralized graph neural network (GNN) critic, trained with multi-agent proximal policy optimization under a centralized training and decentralized execution paradigm. Our pointer-based actor outputs distributions directly over the current task set. This directly supports changing action spaces without masking or retraining. Our GNN critic encodes agent-task interactions as a graph that changes shape with task and agent composition. Together, these components consider AO and TO without the boundedness of existing approaches. We formalize PLATO in a Task-and-Agent-Open Markov Game (TaAgO-MG), extending prior task-open formulations, and prove it is well-defined over the resulting unbounded state and action spaces. We evaluate PLATO with the Methods for Open Agent Systems Evaluation Initiative (MOASEI) wildfire suppression domain, an environment designed for open multi-agent system evaluation, and we demonstrate strong performance and more consistent zero-shot generalization than state-of-the-art baselines in OASYS.