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arxiv: 2605.29693 · v1 · pith:QFFDHUNFnew · submitted 2026-05-28 · 💻 cs.LG · cs.RO

Momentum Based Reward Design for Low Emission Traffic Signal Control

Chinmay Mundane , Amith Manoharan , Arun Singh This is my paper

Pith reviewed 2026-06-29 08:56 UTC · model grok-4.3

classification 💻 cs.LG cs.RO
keywords traffic signal controldeep reinforcement learningreward function designCO2 emissionsSUMO simulatormomentum based rewardadaptive traffic controlthroughput optimization
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The pith

A momentum-based reward for traffic signal reinforcement learning improves throughput-emission trade-offs and learning stability over delay or queue penalties.

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

The paper introduces a Momentum-Based Reward Function for deep reinforcement learning agents that control traffic signals. Instead of penalizing delays or queues, the reward encourages vehicles to keep moving, which is tested in the SUMO simulator against standard reward designs and classical controllers such as Max Pressure and LQF. The results indicate that this produces policies with better balance between sustaining traffic flow and cutting CO2 emissions, plus more consistent training behavior. A sympathetic reader would care because effective adaptive signal control can reduce urban congestion and pollution without building new roads.

Core claim

The Momentum-Based Reward Function produces policies with better throughput-emission trade-offs and more stable learning behavior than delay or queue-based rewards, as well as classical controllers such as Max Pressure and LQF.

What carries the argument

The Momentum-Based Reward Function (MBRF), which rewards continuous vehicle movement rather than penalizing congestion alone.

If this is right

  • DRL agents learn policies that sustain higher throughput while lowering CO2 emissions in the tested scenarios.
  • Training exhibits more stable behavior with reduced variance compared to delay or queue rewards.
  • The method outperforms Max Pressure and LQF controllers on standard traffic metrics in SUMO.
  • Adaptive signal control can respond to dynamic conditions while targeting both flow and emissions.

Where Pith is reading between the lines

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

  • The same momentum principle could be tested in other flow-control tasks such as energy systems or supply chains.
  • Combining MBRF with multi-intersection coordination might extend gains to larger urban networks.
  • Direct comparison against emission-specific rewards would clarify whether momentum is the key driver.

Load-bearing premise

That performance differences observed inside the SUMO simulator will translate to real-world traffic networks and that the momentum-based reward will not introduce unintended side effects such as unsafe signal timings.

What would settle it

A deployment on a real intersection or higher-fidelity simulator where MBRF policies produce higher emissions, lower throughput, or less stable behavior than delay-based rewards or Max Pressure.

Figures

Figures reproduced from arXiv: 2605.29693 by Amith Manoharan, Arun Singh, Chinmay Mundane.

Figure 1
Figure 1. Figure 1: The traffic signal control problem is modeled as a Markov Decision [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Two-way single intersection with through, left, and right options [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 5
Figure 5. Figure 5: Throughput comparison across methods. 0 200 400 600 800 1000 Time step (seconds) 0 20 40 60 Average Travel Time (s) Average Travel Time Comparison Wait Queue Diff Wait Momentum MaxP LQF [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 4
Figure 4. Figure 4: Average queue length during evaluation episodes. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 7
Figure 7. Figure 7: Total CO2 emissions comparison. B. Travel Time Performance Average travel time results further illustrate trade-offs be￾tween objectives. Although the waiting-time reward achieves the lowest travel time (22.6 s), it is accompanied by ex￾tremely poor queue and emission performance. Among the more balanced approaches, the proposed momentum-based reward achieves an average travel time of 41.9 s, improving upo… view at source ↗
read the original abstract

Urban traffic congestion is a growing global issue contributing significantly to long commute times and environmental pollution. Traditional traffic signal control systems often fail to adapt to dynamic traffic conditions. Adaptive traffic signal control can improve urban traffic without changing road infrastructure. Deep Reinforcement Learning (DRL) has shown strong performance for this task, but existing delay and queue-based rewards often produce short-sighted or unstable policies. This paper proposes a Momentum-Based Reward Function (MBRF) that encourages vehicles to keep moving rather than penalizing congestion alone. The method is evaluated in SUMO (Simulation of Urban MObility) using standard traffic metrics such as waiting time, queue length, throughput, and CO2 emissions. Results show that the proposed reward produces better throughput-emission trade-offs and more stable learning behavior than delay or queue-based rewards, as well as classical controllers such as Max Pressure and LQF.

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 / 2 minor

Summary. The manuscript proposes a Momentum-Based Reward Function (MBRF) for deep reinforcement learning in adaptive traffic signal control. The MBRF encourages continuous vehicle movement rather than solely penalizing delay or queues, with the goal of achieving superior throughput-emission trade-offs and more stable learning dynamics. Evaluation occurs in the SUMO simulator using metrics such as waiting time, queue length, throughput, and CO2 emissions, with comparisons to delay/queue-based rewards and classical controllers including Max Pressure and LQF.

Significance. If the simulation results hold under scrutiny, the work could contribute to improved reward engineering for RL-based traffic systems, addressing known limitations of myopic rewards while targeting emission reductions. The use of a standard simulator and conventional metrics provides a reproducible baseline for comparison.

major comments (3)
  1. [Abstract] Abstract: The central claim of better throughput-emission trade-offs and more stable learning is asserted without any quantitative results, error bars, statistical tests, or training details, so the data cannot be checked against the claim.
  2. [Experiments] Experiments/Results sections: No analysis is provided on whether performance differences survive domain shift to real-world networks or remain safe under sensor noise, pedestrian presence, or non-ideal vehicle dynamics; this is load-bearing for the engineering conclusion that MBRF yields low-emission control.
  3. [Method] Method section: The precise formulation of the momentum term (e.g., how velocity or movement history enters the reward) is not visible, preventing assessment of whether it genuinely mitigates the short-sightedness attributed to delay/queue rewards.
minor comments (2)
  1. Add explicit discussion of hyperparameter sensitivity for the momentum coefficient and any ablation studies isolating its contribution.
  2. Ensure all figures include error bars or confidence intervals and that tables report exact numerical values rather than qualitative descriptions.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and indicate where revisions have been or will be made to strengthen the paper.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim of better throughput-emission trade-offs and more stable learning is asserted without any quantitative results, error bars, statistical tests, or training details, so the data cannot be checked against the claim.

    Authors: We agree that the abstract would be strengthened by including quantitative support. The results section reports metrics with error bars from five independent runs and includes statistical comparisons. In the revised manuscript, we have updated the abstract to summarize key quantitative findings on throughput-emission trade-offs and learning stability. revision: yes

  2. Referee: [Experiments] Experiments/Results sections: No analysis is provided on whether performance differences survive domain shift to real-world networks or remain safe under sensor noise, pedestrian presence, or non-ideal vehicle dynamics; this is load-bearing for the engineering conclusion that MBRF yields low-emission control.

    Authors: We acknowledge that robustness under real-world conditions is important for the engineering claims. Our evaluation follows the standard practice in the field by focusing on controlled SUMO simulations for reward design comparison. We have added a dedicated limitations paragraph in the discussion section that explicitly addresses domain shift, sensor noise, pedestrians, and non-ideal dynamics, along with suggested future validation steps. revision: partial

  3. Referee: [Method] Method section: The precise formulation of the momentum term (e.g., how velocity or movement history enters the reward) is not visible, preventing assessment of whether it genuinely mitigates the short-sightedness attributed to delay/queue rewards.

    Authors: The momentum term is defined in the Method section (see Equation 3 and surrounding text) as an additive component that incorporates recent velocity history to reward sustained movement. We will revise the section to present the formulation more prominently, with an expanded derivation showing how it differs from pure delay or queue penalties, and we will include a short algorithmic outline for clarity. revision: yes

standing simulated objections not resolved
  • Empirical evaluation of MBRF performance under real-world domain shifts, sensor noise, pedestrian interactions, or non-ideal vehicle dynamics (these would require new experiments outside the current simulation study).

Circularity Check

0 steps flagged

No circularity: empirical reward proposal with simulation evaluation

full rationale

The paper introduces a Momentum-Based Reward Function (MBRF) as a design choice to encourage continuous vehicle movement and reports comparative SUMO simulation outcomes on throughput, emissions, waiting time, and learning stability versus delay/queue rewards and Max-Pressure/LQF. No derivation chain, fitted parameters renamed as predictions, self-citation load-bearing uniqueness theorems, or ansatz smuggling is present; the performance claims rest on external simulator benchmarks rather than reducing to the reward definition itself by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no information on free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5678 in / 1062 out tokens · 24098 ms · 2026-06-29T08:56:56.621985+00:00 · methodology

discussion (0)

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