Supervisor: Mike Preuss

Summary: The success of AlphaStar for StarCraft has shown that competitive agents learning “together” can enhance the generality of a solution immensely. Can we also use this scheme to improve strategy learning for much simpler settings as in MCTS or similar approaches on the much smaller game microRTS (https://github.com/santiontanon/microrts)?

Supervisor: Aske Plaat

Summary: AlphaZero has taught itself to play world-class Go from scratch using a form of reinforcement learning called curriculum learning. Can we apply curriculum learning in single-agent puzzles such as Sokoban, and thus, in principle, to some of the world’s most important optimization problems?

Supervisor: Thomas Moerland

Summary: Standard planning approaches use forward methods, i.e., they start at the beginning of a problem and repeatedly unfold a search tree in the forward direction. However, that seems counterintuitive to the way humans tend to plan (outside of boardgames like Chess). Instead, we first sample a distant goal, and then repeatedly inpaint different trajectories between start and goal state, for example starting by fixing a goal halfway. In this project, we will look at this new approach towards hierarchical planning (see, e.g., https://arxiv.org/pdf/2006.13205.pdf for a first attempt).

Supervisor: Thomas Moerland

Summary: Exploration is a key topic in reinforcement learning. A popular approach is through the use of intrinsic motivation, which for example explores like children based on curiosity and novelty. However, few of these methods have been goal-conditioned and based on learning progress, better known as competence-based intrinsic motivation (https://arxiv.org/pdf/2012.09830.pdf). In this project, we will investigate new approaches for competence-based exploration, for example extending from http://proceedings.mlr.press/v87/laversanne-finot18a/laversanne-finot18a.pdf.