Understanding reinforcement learned crowds
This work addresses the problem of inconsistent simulation setups in reinforcement learning for crowd animation, providing guidance for researchers in computer graphics to improve design choices, though it is incremental in nature.
The paper analyzes the impact of arbitrary design choices in reinforcement learning for crowd simulation, such as observation and action spaces, on learning performance and energy efficiency. It finds that using neighboring agents' information as observation generally outperforms raycasting, and nonholonomic controls with egocentric observations produce more efficient behaviors than holonomic controls with absolute observations.
Simulating trajectories of virtual crowds is a commonly encountered task in Computer Graphics. Several recent works have applied Reinforcement Learning methods to animate virtual agents, however they often make different design choices when it comes to the fundamental simulation setup. Each of these choices comes with a reasonable justification for its use, so it is not obvious what is their real impact, and how they affect the results. In this work, we analyze some of these arbitrary choices in terms of their impact on the learning performance, as well as the quality of the resulting simulation measured in terms of the energy efficiency. We perform a theoretical analysis of the properties of the reward function design, and empirically evaluate the impact of using certain observation and action spaces on a variety of scenarios, with the reward function and energy usage as metrics. We show that directly using the neighboring agents' information as observation generally outperforms the more widely used raycasting. Similarly, using nonholonomic controls with egocentric observations tends to produce more efficient behaviors than holonomic controls with absolute observations. Each of these choices has a significant, and potentially nontrivial impact on the results, and so researchers should be mindful about choosing and reporting them in their work.