Adaptive Human-AI Coordination via Hierarchical Action Disentanglement

Adnan Ahmad; Bahareh Nakisa; Mohammad Naim Rastgoo

arxiv: 2605.24343 · v1 · pith:NQEQYXBFnew · submitted 2026-05-23 · 💻 cs.AI

Adaptive Human-AI Coordination via Hierarchical Action Disentanglement

Adnan Ahmad , Bahareh Nakisa , Mohammad Naim Rastgoo This is my paper

Pith reviewed 2026-06-30 14:04 UTC · model grok-4.3

classification 💻 cs.AI

keywords Intrinsic Action DisentanglementHierarchical Reinforcement LearningHuman-AI CoordinationOvercooked-AIAction DisentanglementAdaptive AgentsPartner-Aware PoliciesDeep Hierarchical RL

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The pith

Intrinsic Action Disentanglement uses an intrinsic reward to separate low-level behaviors across high-level skills, enabling adaptive coordination with varied human partners.

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

The paper seeks to prevent AI agents from collapsing into one dominant behavior when collaborating with humans who differ in skill and style. It introduces Intrinsic Action Disentanglement (IAD), a hierarchical reinforcement learning approach that adds an intrinsic reward to force the low-level policy to produce distinct action distributions for each high-level skill. This creates clear, interpretable links between the agent's high-level choices and its responses to specific partners while capturing extended interaction patterns. The result is tested in the Overcooked-AI game across simulated partners, human-proxy models, and actual people, where it outperforms baselines in multiple layouts.

Core claim

IAD is a deep hierarchical reinforcement learning framework that learns distinct, partner-aware low-level action sequences conditioned on high-level latent skills. It introduces an intrinsic reward that explicitly encourages disentangled action distributions of the agent's low-level policy across skills, yielding an interpretable mapping between high-level decisions and partner-specific behavioral responses. By capturing temporally extended interaction patterns, IAD enables flexible adaptation to heterogeneous partner dynamics under distributional shift and consistently outperforms strong baselines across Overcooked-AI layouts with unseen simulated partners, a human-proxy model, and real hum

What carries the argument

The intrinsic reward added to the low-level policy in the DHRL framework, which encourages disentangled action distributions across high-level skills to produce partner-specific behaviors.

If this is right

Agents avoid collapsing to a single behavior and instead maintain distinct responses tied to different high-level skills.
Coordination improves with both seen and unseen partners, including real humans, across multiple game layouts.
Temporally extended patterns captured by the hierarchy support adaptation under changes in partner dynamics.
High-level decisions become interpretable through their mapped low-level behavioral responses.

Where Pith is reading between the lines

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

The disentanglement mechanism might transfer to other collaborative domains that require handling varied human inputs without full retraining.
Explicit separation of action distributions could reduce interference between skills in any hierarchical policy trained with humans.
Testing the same intrinsic reward in online, non-stationary human interactions would reveal whether the offline Overcooked gains hold during live play.

Load-bearing premise

The intrinsic reward will reliably create disentangled low-level action distributions and improve adaptation without causing training instability or harming overall task performance.

What would settle it

An ablation experiment in Overcooked-AI that removes the intrinsic reward, retrains the agent, and measures no gain in coordination success rate or disentanglement metric (such as mutual information between skills and action sequences) with real human partners would falsify the claim.

Figures

Figures reproduced from arXiv: 2605.24343 by Adnan Ahmad, Bahareh Nakisa, Mohammad Naim Rastgoo.

**Figure 2.** Figure 2: Average episodic return during training across 30 parallel rollout environments. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Figure 3: 2D UMAP projections of sequence embeddings for Cramped Room and Coord. Ring layouts. Each [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

**Figure 4.** Figure 4: Human and AI-agent trajectories in the Cramped Room layout under different skill activations. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗

**Figure 5.** Figure 5: The five standard Overcooked layouts (left to right): Cramped Room, Asymmetric Advantages, [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗

**Figure 6.** Figure 6: Heatmaps showing the average pairwise returns of a diverse partner population in Overcooked [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗

read the original abstract

Human-AI collaboration requires agents that can adapt to diverse partner behaviors and skill levels while remaining robust to unseen partners. Existing methods often collapse to a single dominant behavior or learn poorly aligned skills, limiting effective coordination. We propose Intrinsic Action Disentanglement (IAD), a deep hierarchical reinforcement learning (DHRL) framework that learns distinct, partner-aware low-level action sequences conditioned on high-level latent skills. IAD introduces an intrinsic reward that explicitly encourages disentangled action distributions of the agent's low-level policy across skills, yielding an interpretable mapping between high-level decisions and partner-specific behavioral responses. By capturing temporally extended interaction patterns, IAD enables flexible adaptation to heterogeneous partner dynamics under distributional shift. We evaluate IAD in the Overcooked-AI domain across multiple layouts and diverse partner settings, including unseen simulated partners, a human-proxy model trained on human-human gameplay, and real human partners. Results show that IAD consistently outperforms strong baselines and achieves more reliable, adaptive coordination across all settings.

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.

Desk Editor's Note private letter to a colleague

IAD adds a specific intrinsic reward for disentangling low-level actions in hierarchical RL for human-AI Overcooked play, but the abstract leaves the reward undefined and supplies no results or ablations.

read the letter

The new piece here is the explicit intrinsic reward inside a deep hierarchical RL setup that pushes the low-level policy toward distinct action distributions across skills. The goal is an interpretable link from high-level choices to partner-specific behaviors, which the authors say helps adaptation under distributional shift.

The evaluation plan looks reasonable on paper: multiple Overcooked layouts, unseen simulated partners, a human-proxy model, and real human players. That mix is better than the usual simulated-only tests in this corner of the field.

The main gap is that the intrinsic reward itself is never described—no equation, no weighting term, no mention of how it trades off against the task reward. The stress-test note is right on this: if the reward is too light, disentanglement probably fails; if too heavy, the agent may stop solving the actual task. Without ablations, sensitivity checks, or stability numbers, the central mechanism stays untested.

The paper is aimed at people already working on hierarchical RL for human-AI coordination in gridworlds. Someone building on Overcooked baselines might pick up the disentanglement idea, but only after seeing the missing implementation details.

I would send it to peer review. The evaluation scope is decent and the framing is clear, even if the current write-up leaves the load-bearing reward choice opaque.

Referee Report

2 major / 1 minor

Summary. The paper proposes Intrinsic Action Disentanglement (IAD), a deep hierarchical reinforcement learning framework for adaptive human-AI coordination. It introduces an intrinsic reward to encourage disentangled action distributions in the low-level policy across high-level latent skills, claiming this yields an interpretable high-level to partner-specific behavior mapping, enables robust adaptation under distributional shift, and produces consistent outperformance over strong baselines in Overcooked-AI across simulated partners, a human-proxy model, and real human partners.

Significance. If the empirical claims hold with proper controls and ablations, the work could advance methods for learning partner-aware hierarchical policies that support flexible coordination with heterogeneous humans. The focus on real-human evaluation and explicit disentanglement via intrinsic rewards would be a concrete contribution if the mechanism is shown to be stable and non-circular.

major comments (2)

[Abstract and §3] Abstract and §3 (Methods): The intrinsic reward is the load-bearing mechanism asserted to produce disentangled low-level policies, interpretable mappings, and outperformance, yet no equation, weighting factor, regularization term, or interaction with the task reward in the DHRL objective is supplied; without this formulation it is impossible to evaluate whether the reward reliably achieves disentanglement or introduces instability/performance trade-offs.
[Abstract and Results] Abstract and Results section: The abstract asserts 'consistent outperformance' and 'more reliable, adaptive coordination' across all settings including real humans, but supplies no quantitative results, error bars, statistical tests, baseline details, or controls; the central claims cannot be assessed without these.

minor comments (1)

[Notation] Clarify notation for 'partner-aware low-level action sequences' and how the high-level latent skills are sampled or conditioned during execution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We address each major point below and commit to revisions that strengthen the manuscript's clarity and completeness.

read point-by-point responses

Referee: [Abstract and §3] Abstract and §3 (Methods): The intrinsic reward is the load-bearing mechanism asserted to produce disentangled low-level policies, interpretable mappings, and outperformance, yet no equation, weighting factor, regularization term, or interaction with the task reward in the DHRL objective is supplied; without this formulation it is impossible to evaluate whether the reward reliably achieves disentanglement or introduces instability/performance trade-offs.

Authors: We agree that the absence of an explicit formulation for the intrinsic reward limits evaluability. In the revised manuscript we will add the full mathematical definition of the intrinsic reward (including its weighting factor relative to the task reward), any regularization terms, and its precise integration into the DHRL objective function within Section 3. This addition will allow readers to assess stability and disentanglement properties directly. revision: yes
Referee: [Abstract and Results] Abstract and Results section: The abstract asserts 'consistent outperformance' and 'more reliable, adaptive coordination' across all settings including real humans, but supplies no quantitative results, error bars, statistical tests, baseline details, or controls; the central claims cannot be assessed without these.

Authors: We acknowledge the concern. The current results section contains performance tables and figures with error bars and baseline comparisons, yet we agree these elements require clearer presentation and additional statistical tests for full transparency. In revision we will expand the results section with explicit quantitative values, p-values or confidence intervals, and further controls, and we will update the abstract to reference key quantitative outcomes where space permits. revision: yes

Circularity Check

0 steps flagged

No circularity: intrinsic reward presented as independent design choice with no reduction to fitted outcomes or self-citations.

full rationale

The provided abstract and description introduce IAD as a DHRL framework whose central mechanism—an intrinsic reward encouraging disentangled low-level action distributions—is stated as an explicit design decision rather than a quantity derived from or fitted to the reported performance metrics. No equations, self-citations, uniqueness theorems, or renamings appear in the given material that would reduce the claimed mapping or outperformance to the inputs by construction. The derivation chain therefore remains self-contained against external benchmarks, consistent with the reader's assessment of score 1.0.

Axiom & Free-Parameter Ledger

1 free parameters · 0 axioms · 1 invented entities

The central claim rests on the effectiveness of a newly introduced intrinsic reward and the hierarchical structure for producing disentanglement and adaptation; these are not supported by external benchmarks or derivations in the abstract.

free parameters (1)

intrinsic reward weight
Hyperparameter balancing the disentanglement objective against task reward, likely tuned during training though not specified.

invented entities (1)

Intrinsic reward for action disentanglement no independent evidence
purpose: To encourage distinct low-level action distributions across high-level skills
New component introduced by the method with no independent evidence provided in the abstract.

pith-pipeline@v0.9.1-grok · 5701 in / 1260 out tokens · 51042 ms · 2026-06-30T14:04:06.832222+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

2 extracted references · 2 canonical work pages · 1 internal anchor

[1]

High-Dimensional Continuous Control Using Generalized Advantage Estimation

doi: 10.1162/neco.1997.9.8.1735. Hengyuan Hu, Adam Lerer, Alex Peysakhovich, and Jakob Foerster. “other-play” for zero-shot coordination. InInternational Conference on Machine Learning, pp. 4399–4410. PMLR, 2020. Gary Klein, David D. Woods, Jeffrey M. Bradshaw, Robert R. Hoffman, and Paul J. Feltovich. Ten challenges for making automation a "team player" ...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.1162/neco.1997.9.8.1735 1997
[2]

Sutton, Doina Precup, and Satinder Singh

doi: https://doi.org/10.1016/S0004-3702(99)00052-1. URLhttps://www.sciencedirect.com/ science/article/pii/S0004370299000521. Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu. Feudal networks for hierarchical reinforcement learning. InInternational conference on machine learning, pp. ...

work page doi:10.1016/s0004-3702(99)00052-1 2017

[1] [1]

High-Dimensional Continuous Control Using Generalized Advantage Estimation

doi: 10.1162/neco.1997.9.8.1735. Hengyuan Hu, Adam Lerer, Alex Peysakhovich, and Jakob Foerster. “other-play” for zero-shot coordination. InInternational Conference on Machine Learning, pp. 4399–4410. PMLR, 2020. Gary Klein, David D. Woods, Jeffrey M. Bradshaw, Robert R. Hoffman, and Paul J. Feltovich. Ten challenges for making automation a "team player" ...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.1162/neco.1997.9.8.1735 1997

[2] [2]

Sutton, Doina Precup, and Satinder Singh

doi: https://doi.org/10.1016/S0004-3702(99)00052-1. URLhttps://www.sciencedirect.com/ science/article/pii/S0004370299000521. Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu. Feudal networks for hierarchical reinforcement learning. InInternational conference on machine learning, pp. ...

work page doi:10.1016/s0004-3702(99)00052-1 2017