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

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

SwitchMT is a novel adaptive task-switching methodology designed for reinforcement learning (RL)-based multi-task learning in intelligent autonomous agents, such as mobile robots. It addresses the limitations of existing methods, which struggle with task interference and rely on fixed context/task-switching intervals, thereby limiting scalability and effectiveness. SwitchMT employs a Deep Spiking Q-Network with active dendrites and a dueling structure, utilizing task-specific context signals to create specialized sub-networks. Crucially, it incorporates an adaptive task-switching policy that monitors both rewards and internal dynamics of network parameters to determine optimal switching points. Experimental results on Atari games (Pong, Breakout, Enduro) demonstrate that SwitchMT achieves competitive scores, such as -8.8 for Pong, 5.6 for Breakout, and 355.2 for Enduro, outperforming several state-of-the-art methods like DQN, DSQN, and MTSpark_ADD, and showing better generalized learning capability.

Key takeaway

Research Scientists developing multi-task reinforcement learning agents should consider integrating adaptive task-switching policies like SwitchMT's. By dynamically adjusting task transitions based on learning progress and internal network dynamics, you can reduce training time, mitigate overfitting, and eliminate the need for extensive hyperparameter tuning for fixed switching intervals, leading to more efficient and scalable generalist agents.

Key insights

SwitchMT improves multi-task learning in autonomous agents via adaptive task-switching and specialized Spiking Neural Networks.

Principles

• Adaptive task-switching enhances multi-task learning efficiency.
• Active dendrites create task-specific sub-networks.
• Monitor parameter changes to detect learning plateaus.

Method

SwitchMT uses a Deep Spiking Q-Network with active dendrites and a dueling structure. It employs an adaptive task-switching policy that monitors the relative change in model parameters over K episodes, switching tasks if the change falls below a predefined threshold (e.g., 10%).

In practice

• Implement SNNs with active dendrites for multi-task RL.
• Monitor model parameter changes for dynamic task scheduling.
• Use dueling Q-networks to improve generalization.

Topics

• Spiking Neural Networks
• Multi-Task Reinforcement Learning
• Adaptive Task-Switching
• Intelligent Autonomous Agents
• Deep Spiking Q-Network

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.