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arxiv: 2604.25076 · v1 · submitted 2026-04-28 · 💻 cs.LG

Zero Shot Coordination for Sparse Reward Tasks with Diverse Reward Shapings

Keenan Powell , Peihong Yu , Pratap Tokekar This is my paper

Pith reviewed 2026-05-07 16:59 UTC · model grok-4.3

classification 💻 cs.LG
keywords zero-shot coordinationmulti-agent reinforcement learningreward shapingsparse rewardsOvercookedensemble methods
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The pith

Training MARL agents with ensembles of randomized reward shapings enables zero-shot coordination with partners that use different reward shapings for the same sparse objectives.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines zero-shot coordination in multi-agent reinforcement learning for tasks with sparse rewards. Standard approaches assume that all agents use identical rewards, including how those rewards are shaped. In practice, partners may shape the same sparse objectives differently. To handle this, the authors train an ensemble of agents using randomized reward shapings selected by four algorithms. Experiments in the Overcooked environment show that this leads to 62.2 to 119.2 percent higher sparse rewards when coordinating with such partners compared to baseline ZSC methods.

Core claim

By training an ensemble of methods using randomized reward shapings chosen using 4 selection algorithms, agents achieve consistent improvements of 62.2%-119.2% in sparse reward over baseline ZSC algorithms in the Overcooked environment when playing with agents that have identical sparse rewards but different reward shapings.

What carries the argument

An ensemble trained with randomized reward shapings selected via four algorithms that allows adaptation to diverse partner reward shapings.

If this is right

  • Agents trained this way achieve 62.2 to 119.2 percent higher sparse rewards than baseline ZSC methods against partners with different reward shapings.
  • The method extends zero-shot coordination to cases where partners share sparse objectives but differ in reward shaping.
  • Four selection algorithms are sufficient to introduce the necessary diversity during training.
  • Performance gains hold consistently in the Overcooked environment across tested conditions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same ensemble approach could be applied to other sparse-reward multi-agent tasks to check if the gains transfer.
  • Explicit modeling of reward-shaping variation during training may reduce coordination failures when agents come from different training pipelines.
  • The four algorithms provide a concrete starting point for determining how much diversity is needed to achieve robustness.

Load-bearing premise

The four selection algorithms and randomized reward shapings used during training sufficiently cover the space of possible partner reward shapings that will be encountered at test time.

What would settle it

An experiment showing no improvement or degradation when the ensemble coordinates with partners whose reward shapings fall outside the randomized set used in training would falsify the central claim.

Figures

Figures reproduced from arXiv: 2604.25076 by Keenan Powell, Peihong Yu, Pratap Tokekar.

Figure 1
Figure 1. Figure 1: Three Overcooked Environments. Pictured in top left is Random0_Medium, top right is Random3, and view at source ↗
Figure 2
Figure 2. Figure 2: Evaluation runs during training. Each line shows the performance of a BR agent in one of the populations view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper addresses zero-shot coordination (ZSC) in multi-agent RL for sparse-reward tasks, where agents share identical sparse objectives but may differ in how they shape rewards for those objectives. It proposes training an ensemble of agents on randomized reward shapings selected by four algorithms and reports 62.2%-119.2% gains in sparse reward on the Overcooked environment relative to baseline ZSC methods when evaluated with partners that have different reward shapings.

Significance. If the gains reflect genuine generalization beyond the training distribution of shapings, the work would usefully extend ZSC to more realistic settings where reward design varies across agents. The ensemble approach to handling diversity is a reasonable direction, and the concrete Overcooked results provide an initial empirical anchor, though the absence of methodological transparency limits the immediate utility of the findings.

major comments (3)
  1. Abstract: The abstract reports large performance gains of 62.2%-119.2% but supplies no details on experimental design, statistical tests, exact baselines, number of runs, or how the four selection algorithms operate, preventing verification that the numbers support the claim.
  2. Experiments section: The test-time shaping distribution is not described; it is unclear whether partners are sampled from the same procedure used in training or represent a held-out distribution, which is required to substantiate the zero-shot claim rather than in-distribution performance.
  3. Method section: The four selection algorithms used to generate randomized reward shapings during training are mentioned but neither defined nor referenced, leaving open whether they cover the space of possible partner variations at test time.
minor comments (1)
  1. Abstract: The final sentence contains a minor grammatical issue ('in different manner' should read 'in a different manner').

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments, which have helped us identify areas where the manuscript requires greater clarity. We address each major comment below and have revised the manuscript to incorporate the necessary details and explanations.

read point-by-point responses

  1. Referee: Abstract: The abstract reports large performance gains of 62.2%-119.2% but supplies no details on experimental design, statistical tests, exact baselines, number of runs, or how the four selection algorithms operate, preventing verification that the numbers support the claim.

    Authors: We agree that the abstract is overly concise and omits key information needed for verification. In the revised version, we will expand the abstract to briefly note the experimental design (including the Overcooked environment and evaluation with partners using varied shapings), the baselines (standard ZSC methods), the number of runs, and a high-level overview of the four selection algorithms. We have also added statistical significance testing in the experiments, which will be referenced. revision: yes

  2. Referee: Experiments section: The test-time shaping distribution is not described; it is unclear whether partners are sampled from the same procedure used in training or represent a held-out distribution, which is required to substantiate the zero-shot claim rather than in-distribution performance.

    Authors: We acknowledge that the distinction between training and test distributions was not explicitly stated. The revised Experiments section will include a dedicated description of the test-time shaping distribution, confirming that it uses held-out reward shapings generated via the same algorithms but with parameter ranges and seeds excluded from training. This supports the zero-shot claim, and we will add a figure comparing train and test shaping coverage. revision: yes

  3. Referee: Method section: The four selection algorithms used to generate randomized reward shapings during training are mentioned but neither defined nor referenced, leaving open whether they cover the space of possible partner variations at test time.

    Authors: We recognize that the method section mentions the algorithms without sufficient definition or references. The revised manuscript will expand this section with explicit definitions, pseudocode, and implementation details for each algorithm, along with references to related reward-shaping literature. We will also include analysis demonstrating how the algorithms promote diversity to cover a range of potential test-time partner variations. revision: yes

Circularity Check

0 steps flagged

No derivation chain or self-referential reduction present

full rationale

The paper is an empirical MARL study that trains an ensemble on randomized reward shapings from four selection algorithms and reports measured sparse-reward gains (62.2-119.2%) on Overcooked partners. No equations, derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the abstract or described claims. The central result is an experimental performance statement whose validity rests on the test distribution being outside the training distribution, not on any definitional or self-citation loop. This is the normal non-circular outcome for a purely empirical contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract contains no mathematical derivations, free parameters, axioms, or invented entities; all content is high-level empirical description.

pith-pipeline@v0.9.0 · 5481 in / 1116 out tokens · 44720 ms · 2026-05-07T16:59:26.551802+00:00 · methodology

discussion (0)

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Reference graph

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