# Zero Shot Coordination for Sparse Reward Tasks with Diverse Reward Shapings

arXiv: https://arxiv.org/abs/2604.25076 · HTML: https://mlsanity.com/paper/2604.25076/

## 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 9 of its 20 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Placed in

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

## Builds on (most cited first, 9 of 9)

- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1811.01458/) (2018)
- [Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications](https://mlsanity.com/paper/2408.10215/) (2024)
- [Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biased](https://mlsanity.com/paper/2302.01605/) (2023)
- [Learning to Play Sequential Games versus Unknown Opponents](https://mlsanity.com/paper/2007.05271/) (2020)
- [Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution](https://mlsanity.com/paper/2208.04957/) (2022)
- [Theory of Mind for Deep Reinforcement Learning in Hanabi](https://mlsanity.com/paper/2101.09328/) (2021)
- [Learning to Play Trajectory Games Against Opponents with Unknown Objectives](https://mlsanity.com/paper/2211.13779/) (2022)
- [On-the-fly Strategy Adaptation for ad-hoc Agent Coordination](https://mlsanity.com/paper/2203.08015/) (2022)

## Abstract (arXiv)

Many Multi-Agent Reinforcement Learning (MARL) agents fail to adapt properly to cooperating with agents trained with the same objectives but different seeds, algorithms, or other training differences. This is the problem of Zero-Shot Coordination (ZSC), which focuses on training agents to cooperate well with unknown agents. ZSC has been studied for a variety of tabular cases and simple games such as Hanabi, achieving excellent results. However, existing solutions to ZSC only consider identical rewards for your trained agents and all future partners. This is not realistic for the trained agents, as they do not consider the problem of cooperating with agents that have identical sparse objectives but shape the rewards for those objectives in different manner. To address this issue, we show how to train an ensemble of methods using randomized reward shapings chosen using 4 selection algorithms. Experiments done on the Overcooked environment demonstrate consistent improvements of 62.2%-119.2% in sparse reward over baseline ZSC algorithms when playing with agents that have identical sparse rewards but different reward shapings.