An attractive asteroid game was described in the paper Learning Policies for Embodied Virtual Agents through Demonstration (2017, Jonathan Dinerstein et al.):
In our first experiment, the virtual agent is a spaceship pilot, The pilot's task is to maneuver the spaceship through random asteroid fields
In theory, this game can be solved with reinforcement learning or, more specifically, with a support vector machine (SVM) and epsilon-regression scheme with a Gaussian kernel. But it seems that this task is harder than it looks like, as the authors of the same paper write
Although many powerful AI and machine learning techniques exist, it remains difficult to quickly create AI for embodied virtual agents.
it is quite challenging to achieve natural-looking behavior since these aesthetic goals must be integrated into the fitness function
I really want to understand how reinforcement learning works. I built a simple game to test this. There are squares falling from the sky and you have the arrow keys to escape.
- Player life
- Survival time
- Maximum survival time