Agent Modeling as Auxiliary Task for Deep Reinforcement Learning

Bilal Kartal; Matthew E. Taylor; Pablo Hernandez-Leal

Agent Modeling as Auxiliary Task for Deep Reinforcement Learning

T0 review · 2 major / 2 minor · reviewed 2026-05-24 · grok-4.3

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Pith's one-line read Modeling other agents' policies as auxiliary tasks stabilizes A3C training and raises expected rewards in multiagent settings.

desk verdict Agent modeling auxiliaries improve A3C results in the two tested domains, but without a capacity-matched control the benefit could come from extra parameters rather than useful transfer. read the letter →

arxiv 1907.09597 v1 pith:2SWIFTFK submitted 2019-07-22 cs.MA cs.LG

Pablo Hernandez-Leal , Bilal Kartal , Matthew E. Taylor This is my paper

classification cs.MAcs.LG

keywords agentmodelingauxiliarytasksdeepreinforcementlearningA3Cmultiagentsystemsactor-criticpolicypredictionbestresponse

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper extends Asynchronous Advantage Actor-Critic (A3C) by adding agent modeling as auxiliary tasks that train the network to predict other agents' policies in addition to its own actor and critic outputs. Two architectures achieve this: one through parameter sharing across agents and one through explicit agent policy features. Experiments cover a cooperative multiagent object transportation problem and a competitive two-player Pommerman variant. In both domains the added tasks produce more stable learning curves and higher expected rewards than baseline A3C when agents learn best responses to each other.

What carries the argument

Agent modeling as auxiliary tasks, realized either by parameter sharing or by agent policy features, which adds policy-prediction heads to the A3C network.

What would settle it

A controlled experiment in a fresh multiagent domain where both proposed architectures produce lower expected rewards or more unstable training than standard A3C.

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Extended reading notes

Core claim

By treating prediction of other agents' policies as auxiliary objectives, the two proposed architectures generate shared representations that improve the main policy's performance over standard A3C, without extra environment access or domain-specific tuning.

Load-bearing premise

That learning other agents' policies as auxiliary tasks will produce representations that transfer usefully to the main policy without requiring domain-specific tuning or additional environment access.

Editorial extensions

If this is right

The architectures stabilize learning in both cooperative and competitive multiagent problems.
They deliver higher expected rewards than baseline A3C when learning best responses.
The auxiliary modeling works without domain-specific tuning or extra environment access.
The same auxiliary-task idea applies to coordinated transportation tasks and to zero-sum games such as Pommerman.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same auxiliary modeling heads could be attached to other actor-critic algorithms beyond A3C.
The approach may reduce the sample complexity of learning against previously unseen opponents.
Scaling the method to larger teams would test whether the auxiliary prediction cost remains manageable.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

read the letter

The main point is that this paper adds two ways to treat other agents' policies as auxiliary tasks inside A3C and reports more stable training plus higher expected rewards than plain A3C in both a cooperative transport task and a competitive mini-Pommerman game. The architectures are parameter sharing across the modeling head and a separate policy-features route; both keep the standard actor-critic losses while adding the auxiliary prediction objective. That combination is the concrete new piece, and testing it across cooperative and competitive settings is a reasonable choice. The results are presented as straightforward empirical gains without overclaiming a new framework. The soft spot is exactly the one the stress-test note flags: the added heads increase parameter count and change the gradient flow, yet there is no control that adds similar extra capacity with a non-semantic auxiliary task. Without that, it is hard to know whether the reported improvement comes from learning transferable agent representations or from regularization and extra modeling power. The paper does not appear to close this gap, so the transfer assumption stays untested against simpler alternatives. The rest of the setup looks standard for the area, with no circularity or obvious internal contradictions. This is useful reading for people already running multi-agent actor-critic experiments who want a quick auxiliary-task trick to try. It is not foundational, but the empirical comparison is worth checking in detail. I would send it to peer review because the idea is clean, the domains are sensible, and the results are positive enough to merit referee scrutiny even if the mechanism needs tighter controls.

Referee Report

2 major / 2 minor

Summary. The paper proposes extending Asynchronous Advantage Actor-Critic (A3C) with agent modeling as auxiliary tasks via two architectures—one using parameter sharing and one using agent policy features—to learn other agents' policies alongside the standard actor-critic objectives. Experiments are reported in a cooperative multi-agent object transportation task and a competitive two-player mini-Pommerman domain, with the central claim that the architectures stabilize learning and yield higher expected rewards than baseline A3C when learning best responses.

Significance. If the empirical gains are attributable to transferable representations from agent modeling rather than incidental capacity or regularization effects, the work would offer a practical auxiliary-task method for multi-agent deep RL that builds on representation-learning ideas without requiring explicit opponent modeling at test time. The cooperative and competitive domains provide a reasonable testbed for both settings.

major comments (2)

[Experiments] Experiments section: the headline claim that the architectures 'stabilize learning and outperform the standard A3C architecture' rests on the auxiliary agent-modeling signal producing useful features. No control is described that matches total parameter count and gradient structure while using a non-semantic auxiliary task (e.g., predicting fixed random policies or unrelated observations); without it, observed improvements could arise from added capacity, altered loss magnitudes, or implicit regularization rather than the intended modeling benefit.
[Proposed Architectures] The description of the two architectures (parameter sharing vs. agent policy features) does not include an explicit comparison of their representational capacity or an ablation that isolates the contribution of each component to the reported reward gains; this makes it difficult to attribute performance differences specifically to agent modeling.

minor comments (2)

[Introduction] The abstract and introduction would benefit from a clearer statement of the precise auxiliary loss formulations and how they are combined with the A3C objective (e.g., weighting coefficients).
[Experiments] Figure captions and experimental tables should report the number of independent runs, random seeds, and statistical significance tests for the reported expected-reward differences.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment below and indicate planned revisions.

read point-by-point responses

Referee: [Experiments] Experiments section: the headline claim that the architectures 'stabilize learning and outperform the standard A3C architecture' rests on the auxiliary agent-modeling signal producing useful features. No control is described that matches total parameter count and gradient structure while using a non-semantic auxiliary task (e.g., predicting fixed random policies or unrelated observations); without it, observed improvements could arise from added capacity, altered loss magnitudes, or implicit regularization rather than the intended modeling benefit.

Authors: We agree that the absence of a control experiment using a non-semantic auxiliary task leaves open the possibility that gains arise from capacity or regularization effects rather than the agent-modeling objective. The manuscript reports comparisons only against baseline A3C. We will add such a control (e.g., an auxiliary task predicting fixed random policies) in the revised experiments to better isolate the contribution of agent modeling. revision: yes
Referee: [Proposed Architectures] The description of the two architectures (parameter sharing vs. agent policy features) does not include an explicit comparison of their representational capacity or an ablation that isolates the contribution of each component to the reported reward gains; this makes it difficult to attribute performance differences specifically to agent modeling.

Authors: The paper presents both architectures and directly compares their performance against each other and the baseline in the reported domains. We did not, however, include an explicit analysis of representational capacity (such as parameter counts) or component ablations. We will add a discussion of model sizes and parameter counts in the revision; a more extensive component ablation will be considered if space allows. revision: partial

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical architecture comparison with independent benchmarks

full rationale

The paper proposes two auxiliary-task architectures (parameter sharing; policy features) for agent modeling inside A3C and reports experimental reward curves in two domains. No equations, uniqueness theorems, or closed-form predictions exist that could reduce to self-defined quantities. All performance claims rest on direct comparison against unmodified A3C under the same training protocol; the auxiliary losses are not fitted parameters renamed as predictions. Self-citations, if present, are not load-bearing for any derivation. The work is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

The abstract provides no explicit free parameters, axioms, or invented entities; all modeling choices (network architecture, loss weighting, training schedule) are implicit and would be free parameters in any implementation.

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Cite this review

Pith. "Pith review of Agent Modeling as Auxiliary Task for Deep Reinforcement Learning." pith.science (2026). https://pith.science/paper/2SWIFTFK

@misc{pith2026190709597,
  author       = {Pith},
  title        = {Pith review of: Agent Modeling as Auxiliary Task for Deep Reinforcement Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2SWIFTFK}},
  note         = {Machine review of arXiv:1907.09597}
}

read the original abstract

In this paper we explore how actor-critic methods in deep reinforcement learning, in particular Asynchronous Advantage Actor-Critic (A3C), can be extended with agent modeling. Inspired by recent works on representation learning and multiagent deep reinforcement learning, we propose two architectures to perform agent modeling: the first one based on parameter sharing, and the second one based on agent policy features. Both architectures aim to learn other agents' policies as auxiliary tasks, besides the standard actor (policy) and critic (values). We performed experiments in both cooperative and competitive domains. The former is a problem of coordinated multiagent object transportation and the latter is a two-player mini version of the Pommerman game. Our results show that the proposed architectures stabilize learning and outperform the standard A3C architecture when learning a best response in terms of expected rewards.

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Reviewed May 24, 2026 · model on record in the stance chip above.