Scalable Multi-Task Learning through Spiking Neural Networks with Adaptive Task-Switching Policy for Intelligent Autonomous Agents
Summary: arXiv:2504.13541v5 Announce Type: replace-cross
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.
Key Ideas of SwitchMT
SwitchMT employs the following key ideas:
- Deep Spiking Q-Network: Utilizes active dendrites and a dueling structure, leveraging task-specific context signals to create specialized sub-networks.
- Adaptive Task-Switching Policy: This policy takes into account both rewards and the internal dynamics of the network parameters to optimize task-switching intervals.
Experimental Results
Experimental results demonstrate that SwitchMT achieves competitive scores in multiple Atari games, reflecting its effectiveness in multi-task learning:
- Pong: -8.8
- Breakout: 5.6
- Enduro: 355.2
These results indicate that SwitchMT not only enhances performance in terms of scores but also enables longer game episodes when compared to state-of-the-art methodologies.
Impact on Intelligent Autonomous Agents
The SwitchMT methodology addresses the challenge of task interference without increasing network complexity. This capability is essential for developing intelligent autonomous agents that can efficiently learn multiple tasks in real-time applications. The adaptive task-switching policy allows these agents to dynamically adjust their learning strategies based on the context of the tasks they are performing, ultimately leading to more effective decision-making processes.
Conclusion
In conclusion, the proposed SwitchMT framework offers a promising solution for enhancing multi-task learning in resource-constrained autonomous agents. By integrating adaptive task-switching and advanced neural network structures, SwitchMT sets a new benchmark in the field of reinforcement learning and spiking neural networks. The implications of this research extend to various applications, paving the way for more robust and adaptable intelligent systems in diverse environments.