no code implementations • 5 Jan 2023 • Wenqian Xue, Bosen Lian, Jialu Fan, Tianyou Chai, Frank L. Lewis
In this paper, we formulate inverse reinforcement learning (IRL) as an expert-learner interaction whereby the optimal performance intent of an expert or target agent is unknown to a learner agent.
no code implementations • 29 Mar 2021 • Pan Wang, Zhifeng Gong, Shuo Wang, Hao Dong, Jialu Fan, Ling Li, Peter Childs, Yike Guo
To modify a design semantic of a given product from personalised brain activity via adversarial learning, in this work, we propose a deep generative transformation model to modify product semantics from the brain signal.
no code implementations • 10 Feb 2021 • Pan Wang, Rui Zhou, Shuo Wang, Ling Li, Wenjia Bai, Jialu Fan, Chunlin Li, Peter Childs, Yike Guo
For this reason, we propose an end-to-end brain decoding framework which translates brain activity into an image by latent space alignment.