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arxiv: 2504.13541 · v5 · submitted 2025-04-18 · 💻 cs.NE · cs.AI· cs.LG· cs.RO

Scalable Multi-Task Learning through Spiking Neural Networks with Adaptive Task-Switching Policy for Intelligent Autonomous Agents

Rachmad Vidya Wicaksana Putra , Avaneesh Devkota , Muhammad Shafique This is my paper

Pith reviewed 2026-05-22 19:38 UTC · model grok-4.3

classification 💻 cs.NE cs.AIcs.LGcs.RO
keywords Spiking Neural NetworksMulti-Task LearningReinforcement LearningAdaptive Task SwitchingAtari GamesDueling Q-NetworksAutonomous Agents
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The pith

An adaptive task-switching policy in spiking neural networks allows competitive performance across multiple Atari games without increasing network complexity.

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

The paper proposes SwitchMT for training spiking neural networks simultaneously on several reinforcement learning tasks. It pairs a deep spiking Q-network that uses task-specific signals to build specialized sub-networks with a switching policy that adjusts intervals according to rewards and internal parameter changes. The goal is to cut task interference that otherwise hurts multi-task results. A reader would care because the approach keeps the model small enough for low-power autonomous agents that must handle varied real-world situations at once.

Core claim

SwitchMT employs a Deep Spiking Q-Network with active dendrites and dueling structure that utilizes task-specific context signals to create specialized sub-networks, together with an adaptive task-switching policy that leverages both rewards and internal dynamics of the network parameters. This combination supports effective simultaneous multi-task learning, producing competitive scores on Atari games such as Pong at -8.8, Breakout at 5.6, and Enduro at 355.2, plus longer episodes than prior methods, all without raising network complexity.

What carries the argument

The adaptive task-switching policy, which decides when to change tasks by tracking both external rewards and shifts in network parameters to limit interference during joint training of the spiking architecture.

If this is right

  • Longer episodes and competitive scores on Pong, Breakout, and Enduro compared with earlier multi-task methods.
  • Task interference reduced in simultaneous training without any increase in model size.
  • Low-power spike-based processing preserved for resource-limited agents.
  • Scalable simultaneous learning across diverse tasks for autonomous systems.

Where Pith is reading between the lines

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

  • The same switching logic could be tested on continuous-control environments to check generality beyond discrete Atari actions.
  • Integration with neuromorphic hardware might further cut energy use in deployed agents.
  • The policy could be combined with task-embedding methods to handle even larger sets of related tasks.

Load-bearing premise

Monitoring rewards together with internal network parameter changes is enough to choose task switches that reliably cut interference.

What would settle it

Repeating the Atari experiments using fixed instead of adaptive task intervals and obtaining equal or higher scores and episode lengths.

Figures

Figures reproduced from arXiv: 2504.13541 by Avaneesh Devkota, Muhammad Shafique, Rachmad Vidya Wicaksana Putra.

Figure 1
Figure 1. Figure 1: Multi-task learning performance of the state-of [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our novel contributions in this work. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Our SwitchMT methodology and its key steps: network architecture selection and adaptive task-switching policy. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The integrate-and-fire (IF) neuron model is en [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The network architecture used in our proposed [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Performance of different models (i.e., DQN [ [PITH_FULL_IMAGE:figures/full_fig_p005_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Performance evaluation of DQN_D [30], MTSpark_ADD [5], and SwitchMT on (a) Pong, (b) Breakout, and (c) Enduro. Here, a higher game point means better performance, and often means longer game episodes [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
read the original abstract

Training resource-constrained autonomous agents on multiple tasks simultaneously is crucial for adapting to diverse real-world environments. Recent works employ reinforcement learning (RL) approach, but they still suffer from sub-optimal multi-task performance due to task interference. State-of-the-art works employ Spiking Neural Networks (SNNs) to improve RL-based multi-task learning and enable low-power/energy operations through network enhancements and spike-driven data stream processing. However, they rely on fixed task-switching intervals during its training, thus limiting its performance and scalability. To address this, we propose SwitchMT, a novel methodology that employs adaptive task-switching for effective, scalable, and simultaneous multi-task learning. SwitchMT employs the following key ideas: (1) leveraging a Deep Spiking Q-Network with active dendrites and dueling structure, that utilizes task-specific context signals to create specialized sub-networks; and (2) devising an adaptive task-switching policy that leverages both rewards and internal dynamics of the network parameters. Experimental results demonstrate that SwitchMT achieves competitive scores in multiple Atari games (i.e., Pong: -8.8, Breakout: 5.6, and Enduro: 355.2) and longer game episodes as compared to the state-of-the-art. These results also highlight the effectiveness of SwitchMT methodology in addressing task interference without increasing the network complexity, enabling intelligent autonomous agents with scalable multi-task learning capabilities.

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

1 major / 1 minor

Summary. The paper proposes SwitchMT, a methodology for simultaneous multi-task reinforcement learning using spiking neural networks. It introduces a Deep Spiking Q-Network with active dendrites and dueling structure that incorporates task-specific context signals to form specialized sub-networks, paired with an adaptive task-switching policy that uses both rewards and internal network parameter dynamics. The central claim is that this approach mitigates task interference in multi-task training without increasing network complexity, yielding competitive Atari game scores (Pong: -8.8, Breakout: 5.6, Enduro: 355.2) and longer episodes relative to prior work.

Significance. If validated, the adaptive policy could meaningfully advance scalable, energy-efficient multi-task RL for autonomous agents by enabling simultaneous training with reduced interference. The emphasis on SNNs for low-power operation aligns with practical constraints in real-world deployment, and the reported longer episode lengths suggest potential gains in training stability if the policy's contribution can be isolated.

major comments (1)
  1. [Experimental results] Experimental results section: the reported scores (Pong: -8.8, Breakout: 5.6, Enduro: 355.2) and longer episode claim are presented without ablation studies, interference metrics (e.g., gradient conflict rates or per-task performance drops when adding tasks), or fixed-vs-adaptive task-switching comparisons. This leaves open whether the adaptive policy (rather than the base Deep Spiking Q-Network with active dendrites and dueling structure) is responsible for addressing task interference, undermining the central methodological contribution.
minor comments (1)
  1. [Abstract] The abstract states specific numerical results but supplies no experimental protocol, baseline details, or statistical information (error bars, number of runs); this information should be added for reproducibility even if moved to the main text.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential of the adaptive task-switching policy in advancing scalable multi-task RL with SNNs. We address the major comment below.

read point-by-point responses

  1. Referee: [Experimental results] Experimental results section: the reported scores (Pong: -8.8, Breakout: 5.6, Enduro: 355.2) and longer episode claim are presented without ablation studies, interference metrics (e.g., gradient conflict rates or per-task performance drops when adding tasks), or fixed-vs-adaptive task-switching comparisons. This leaves open whether the adaptive policy (rather than the base Deep Spiking Q-Network with active dendrites and dueling structure) is responsible for addressing task interference, undermining the central methodological contribution.

    Authors: We thank the referee for this observation. The manuscript compares SwitchMT results against state-of-the-art methods that explicitly rely on fixed task-switching intervals (as detailed in the introduction and related work). The reported competitive scores and longer episode lengths therefore reflect performance under adaptive versus fixed switching. We agree that dedicated ablations isolating the adaptive policy, along with quantitative interference metrics such as gradient conflicts or per-task drops, would more clearly attribute gains to the policy rather than the base DSQN architecture. We will add these experiments and analyses to the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical methodology

full rationale

The paper presents SwitchMT as a novel empirical methodology combining a Deep Spiking Q-Network architecture with an adaptive task-switching policy, validated through reported performance scores on Atari games (Pong, Breakout, Enduro) against state-of-the-art baselines. No mathematical derivations, equations, or first-principles predictions are described in the provided text that reduce to inputs by construction, fitted parameters renamed as outputs, or self-citation chains. The work is self-contained against external benchmarks via experimental outcomes, with no load-bearing steps that equate the claimed result to its own assumptions or prior self-references.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, background axioms, or new postulated entities are enumerated in the provided text. The 'active dendrites' and 'adaptive task-switching policy' appear as methodological choices rather than independently evidenced inventions.

pith-pipeline@v0.9.0 · 5810 in / 1207 out tokens · 86353 ms · 2026-05-22T19:38:33.507416+00:00 · methodology

discussion (0)

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Reference graph

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