SwitchMT: Adaptive Task-Switching for Multi-Task Learning in Spiking Neural Networks
A novel methodology called SwitchMT has been introduced to enhance multi-task learning for autonomous agents. It addresses limitations in current reinforcement learning approaches, which often suffer from sub-optimal performance due to task interference. SwitchMT employs adaptive task-switching policies within Spiking Neural Networks, moving beyond fixed intervals that restrict scalability. The method leverages a Deep Spiking Q-Network with active dendrites and a dueling structure to process task-specific context. This advancement aims to improve simultaneous learning across multiple tasks while enabling low-power operations through spike-driven data processing. The research was documented in arXiv preprint 2504.13541v5, categorized as a replace-cross announcement. By optimizing task-switching dynamically, SwitchMT seeks to boost the efficiency and adaptability of intelligent agents in diverse real-world environments.
Key facts
- SwitchMT is a novel methodology for multi-task learning
- It uses adaptive task-switching in Spiking Neural Networks
- Addresses sub-optimal performance from task interference in reinforcement learning
- Employs a Deep Spiking Q-Network with active dendrites and dueling structure
- Aims to enable low-power, energy-efficient operations
- Documented in arXiv preprint 2504.13541v5
- Announcement type is replace-cross
- Focuses on training resource-constrained autonomous agents
Entities
Institutions
- arXiv