Training Observable Control Policies to Expose Agent State Through Actions

Andres Enriquez Fernandez; John J. Bird

arxiv: 2606.27609 · v1 · pith:F4SA4KWBnew · submitted 2026-06-25 · 💻 cs.LG · cs.SY· eess.SY

Training Observable Control Policies to Expose Agent State Through Actions

Andres Enriquez Fernandez , John J. Bird This is my paper

Pith reviewed 2026-06-29 01:13 UTC · model grok-4.3

classification 💻 cs.LG cs.SYeess.SY

keywords reinforcement learningobservabilityagent state estimationaircraft trackingcontrol policiesaction observabilitycommunication constraints

0 comments

The pith

Reinforcement learning trains control policies to expose agent state through actions.

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

The paper explores how agents can communicate their internal state indirectly by shaping their actions, even when direct communications are limited. It uses reinforcement learning with a modified reward that encourages actions to be more informative for external state estimation. Simulation results from an aircraft tracking task show that this enhanced observability can be achieved while keeping the primary task performance nearly unchanged. This matters for scenarios like remote monitoring of autonomous systems where bandwidth or security restricts explicit data sharing. The approach demonstrates that the dual goals of task completion and state exposure can be balanced in policy training.

Core claim

The central claim is that reinforcement learning policies can be trained to make the agent's state more observable from its actions by adding an observability term to the reward function. In the aircraft tracking problem, the trained policy achieves better state estimation for observers while having minimal impact on the nominal tracking performance.

What carries the argument

An additional reward term in the reinforcement learning objective that encourages the policy to select actions which facilitate accurate estimation of the agent's state by an external observer.

If this is right

External observers can reconstruct more agent state information solely from observed actions.
Multi-agent systems can coordinate better under communication limitations.
Autonomous agents can be monitored with reduced need for dedicated communication channels.
The training method allows balancing task performance against information exposure.

Where Pith is reading between the lines

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

Applying this to real-world robotic systems with sensor noise might require adjustments to the reward weighting.
Exploring different estimation techniques beyond the one used in simulation could strengthen the method's generality.
Investigating the effect on policy robustness to environmental changes would be a natural next step.

Load-bearing premise

Adding a reward term for observability does not materially degrade the agent's performance on its primary control task.

What would settle it

If experiments show that any positive weight on the observability reward causes a large increase in tracking error in the aircraft scenario, the claim would be falsified.

read the original abstract

Physical or operational constraints often impose communications limitations on autonomous agents. Such limitations complicate monitoring or multiagent coordination. Even when strong communications are absent, some information may still be available. The remainder of the relevant agent state may be reconstructed via estimation. The actions taken by an agent are a potential source of information -- as the agent interacts with the environment, these actions may be observed even in the absence of explicit communication. We investigate using actions to estimate the state of an agent, using reinforcement learning to develop policies which make the estimation problem more tractable. Policy observability is encouraged through the training reward and is analyzed using simulation of the trained agent. In an aircraft tracking problem a policy with enhanced observability is found that has minimal impact on nominal task performance.

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

The paper shows an RL policy for aircraft tracking can be trained with an added reward term to make actions more informative about hidden state while keeping nominal task performance intact in simulation.

read the letter

The core result here is that the authors added an observability term to the training reward and found a policy in their aircraft tracking simulation where state exposure through actions improved without much cost to the primary control objective. That directly tackles a practical limit in autonomous systems where communications are restricted but actions can still be observed.

What the work does is apply reinforcement learning to shape actions as an information source for downstream estimation. The simulation demonstrates that the two goals can be compatible under their procedure, which is the kind of engineering data that matters for monitoring or coordination setups.

The approach is straightforward and the reported outcome is useful as a proof of concept in this domain. It gives a concrete example rather than just arguing the idea in theory.

The main limitation is the thin description: no details on the exact form of the observability reward, how they quantified the improvement, what baselines were used, or any error bars or sensitivity checks. Without those, it's difficult to judge whether the compatibility holds beyond this one simulation or if it depends on specific tuning. The abstract also skips prior work on observable policies or information-regularized RL, so the incremental step is hard to place.

This is for people working on RL in control tasks with partial observability or limited comms, especially if they need to extract state info from visible actions. A reader already thinking about estimation from behavior would find the empirical case worth seeing.

It should go to peer review. The claim is narrow but the setup is real enough that referees can push on the method and see if the result generalizes.

Referee Report

1 major / 1 minor

Summary. The paper claims that reinforcement learning can be used to train observable control policies by adding a reward term that encourages actions to expose the agent's internal state. This facilitates external state estimation under communication constraints. The approach is evaluated via simulation in an aircraft tracking problem, where a policy is reported to achieve enhanced observability while having minimal impact on nominal task performance.

Significance. If the empirical result holds under the reported training procedure, the work demonstrates compatibility between an observability objective and a primary control task in a concrete simulation setting. This provides an existence proof for policies that turn actions into a passive information source, which may be useful for monitoring or coordination in bandwidth-limited autonomous systems. The simulation-based validation is a positive aspect, though the single-domain result limits immediate generalizability.

major comments (1)

[Abstract] Abstract: the central claim that the observability-enhanced policy has 'minimal impact on nominal task performance' is load-bearing for the compatibility result, yet the provided text supplies no quantitative metrics, baseline comparisons, variance estimates, or statistical tests to support the 'minimal' qualifier or the degree of observability improvement.

minor comments (1)

The manuscript would benefit from explicit description of the observability reward formulation, the state estimator used for analysis, and any hyperparameter choices that affect the trade-off between the two objectives.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We appreciate the referee's constructive feedback and recommendation for minor revision. We address the major comment below.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that the observability-enhanced policy has 'minimal impact on nominal task performance' is load-bearing for the compatibility result, yet the provided text supplies no quantitative metrics, baseline comparisons, variance estimates, or statistical tests to support the 'minimal' qualifier or the degree of observability improvement.

Authors: We agree that the abstract, due to length constraints, does not include quantitative metrics, baseline comparisons, variance estimates, or statistical tests to support the 'minimal impact' claim or the degree of observability improvement. The manuscript body provides these details in the results section. To address this, we will revise the abstract to include key quantitative findings supporting the claim. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper reports an empirical RL training result in which an observability reward term is added to the objective for an aircraft tracking task; the central claim is that the resulting policy exposes more state information through actions while preserving nominal performance. This is demonstrated directly by the existence of the trained policy in simulation rather than by any derivation, fitted parameter, or self-citation chain that reduces to its own inputs. No equations, uniqueness theorems, or ansatzes appear in the provided text, and the compatibility of the two objectives is shown by the reported outcome itself rather than presupposed.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review; the central claim rests on the unelaborated domain assumption that actions carry usable state information and that a composite reward can balance observability against task performance. No free parameters, additional axioms, or invented entities are mentioned.

axioms (1)

domain assumption Actions taken by an agent interacting with the environment can serve as a source of information about its internal state even without explicit communication.
Stated directly in the abstract as the premise enabling the approach.

pith-pipeline@v0.9.1-grok · 5656 in / 1133 out tokens · 39738 ms · 2026-06-29T01:13:17.114626+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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Panait, L., and Luke, S., “Cooperative multi-agent lear ning: The state of the art,” Autonomous agents and multi-agent systems, Vol. 11, 2005, pp. 387–434. https://doi.org/10.1007/s104 58-005-2631-2

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work page doi:10.2514/6.2024-0989 2024

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Predictive decision making driven by multiple time-linked reward representations in the anterior cingulate cortex,

Wittmann, M. K., Kolling, N., Akaishi, R., Chau, B. K., B rown, J. W., Nelissen, N., and Rushworth, M. F., “Predictive decision making driven by multiple time-linked reward representations in the anterior cingulate cortex,” Nature Communications, Vol. 7, No. 1, 2016, pp. 1–13. https://doi.org/10.1038/ncomms123 27

work page doi:10.1038/ncomms123 2016

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State estimation in the cere bellum,

Miall, R. C., and King, D., “State estimation in the cere bellum,” Cerebellum, Vol. 7, No. 4, 2008, pp. 572–576. https://doi.org/10.1007/s12311-008-0072-6

work page doi:10.1007/s12311-008-0072-6 2008

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Why can’ t you tickle yourself?

Blakemore, S.-J., Wolpert, D., and Frith, C., “Why can’ t you tickle yourself?” NeuroReport, Vol. 11, No. 11, 2000, pp. 11–16. https://doi.org/10.1038/news981022-7

work page doi:10.1038/news981022-7 2000

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work page doi:10.1016/0016-0 1988