We present a reinforcement learning framework for crowd simulation that balances collision avoidance with navigation preferences guided by soft environmental constraints. Our approach integrates two complementary perception components: entity perception, which handles hard constraints imposed by physical obstacles, and implicit environmental perception, encoded as suitability maps that guide movement preferences due to soft constraints. To ensure robust generalization across complex scenes, we employ a modular, two-phase training strategy utilizing curriculum-based environmental templates. The proposed framework functions as an intuitive crowd authoring tool: artists can influence crowd behavior in real time by interactively editing suitability maps or dynamically adjusting perception weights, either globally or per-agent. This enables adaptive navigation behaviors, such as dynamic trajectory prioritization based on the environment, to emerge from local interactions. We validate our approach using both quantitative metrics and qualitative analysis, including a user study confirming that the resulting behaviors align with pedestrian traffic patterns observed in real-world settings. We further present an example of emergent social behavior through the formation and gradual evolution of desire paths. This work contributes to the field of crowd simulation by offering a robust, learning-based framework that supports heterogeneous navigation modeling and intuitive authoring for interactive applications.

Rule-breaking behaviors, such as jaywalking or skipping queues, are common in crowds but difficult to study in real-world settings due to limited control and observability. Virtual reality (VR) provides a controlled alternative, but its validity depends on whether VR elicits realistic rule-breaking behavior. We conducted a VR study with 65 participants navigating a virtual city with four scenarios differing in social norms: walking on grass, crossing outside a crosswalk, jaywalking at a red light, and skipping a line. In each scenario, the proportion of rule-breaking virtual characters was manipulated (0%, 25%, 50%). Participants' movements and gaze were recorded to assess behavior and attention. Results showed higher rule-breaking as the number of violators increased, except in the low-stakes crossing scenario. Rule-breakers attended more to violating characters, whereas rule-followers focused on compliant ones. Participants cited efficiency, safety, and social norms as key factors guiding their decisions. Overall, VR reproduced natural patterns of social compliance and noncompliance, supporting its use for studying rule-breaking and applications in crowd simulation, urban design, safety training, and immersive media.