Zhao Yang

I am a PhD student at the Reinforcement Learning Group, Leiden University, supervised by Thomas Moerland, Mike Preuss, and Aske Plaat. I got my master degree at Leiden University in 2020, and bachelor degree at BLCU, China, in 2018.

I'm interested in reinforcement learning and trying to automate agents using [intrinsic motivation, world models...], mostly in games and robotic tasks.

I co-host BeNeRL seminar, assist courses DRL, SADRL, VG4R, and review.

Contact: z.yang(at)liacs.leidenuniv.nl
Google Scholar | LinkedIn | Twitter | Github | CV

I'm actively looking for internships / jobs / postdocs!

Research

	An Autonomous RL Agent In Submission Keywords: World Models, Autonomy, Unsupervised RL Website
	Two-Memory Reinforcement Learning Zhao Yang, Thomas M. Moerland, Mike Preuss, Aske Plaat COG, 2023; EWRL, 2023 Paper \| Code Combine episodic control (EC) and RL together. The agent learns to automatically switch between EC and RL.
	Continuous Episodic Control Zhao Yang, Thomas M. Moerland, Mike Preuss, Aske Plaat COG, 2023; EWRL, 2023 Paper \| Code Use episodic memory directly for continuous action selection. It outperforms SOTA RL agents.
	First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation Zhao Yang, Thomas M. Moerland, Mike Preuss, Aske Plaat ICAART, 2023; ALOE workshop @ICLR, 2022 Paper Systematically illustrate that why and how Go-Explore works in tabular and deep RL settings. Explore ('exp') can help the agent step into unseen areas.
	Transfer Learning and Curriculum Learning in Sokoban Zhao Yang, Mike Preuss, Aske Plaat BNAIC, 2021 Paper \| Code Pre-train and fine-tune neural networks on Sokoban tasks. Agents pre-trained in 1-box tasks can learn faster in 2/3-box tasks, but not vice versa.

Latest update: 02/2024