Multi-Task Deep Reinforcement Learning for Continuous Action Control
Multi-Task Deep Reinforcement Learning for Continuous Action Control
Zhaoyang Yang, Kathryn Merrick, Hussein Abbass, Lianwen Jin
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3301-3307.
https://doi.org/10.24963/ijcai.2017/461
In this paper, we propose a deep reinforcement learning algorithm to learn multiple tasks concurrently. A new network architecture is proposed in the algorithm which reduces the number of parameters needed by more than 75% per task compared to typical single-task deep reinforcement learning algorithms. The proposed algorithm and network fuse images with sensor data and were tested with up to 12 movement-based control tasks on a simulated Pioneer 3AT robot equipped with a camera and range sensors. Results show that the proposed algorithm and network can learn skills that are as good as the skills learned by a comparable single-task learning algorithm. Results also show that learning performance is consistent even when the number of tasks and the number of constraints on the tasks increased.
Keywords:
Machine Learning: Reinforcement Learning
Machine Learning: Transfer, Adaptation, Multi-task Learning
Machine Learning: Deep Learning