Fang, F., Liang, W., Wu, Y., Xu, Q., & Lim, J.-H. (2022). Improving Generalization of Reinforcement Learning Using a Bilinear Policy Network. 2022 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip46576.2022.9897349
Abstract:
In deep reinforcement learning (DRL), the agent is usually rained on seen environments by optimizing a policy network. However, it is difficult to be generalized to unseen environments properly, even when the environmental variations are insignificant. This is partly because the policy network cannot effectively learn the representation of visual difference that is subtle among highly similar states in the environments. Because a bilinear structured model containing two feature extractors allows pairwise feature interactions in a translationally invariant manner which makes it particularly useful for subtle difference recognition among highly similar states, in this work, a bilinear policy network is employed to enhance representation learning, and thus to improve generalization of the DRL. The proposed bilinear policy network is tested on various DRL task, including a control task on path planning for active object detection, and Grid World, an AI game task.
The test results show that the generalization of DRL can be improved by the proposed network.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - AME Programmatic Funding Scheme
Grant Reference no. : A18A2b0046