Crowd simulation by deep reinforcement learning

Simulating believable virtual crowds has been an important research topic in many research fields such as industry films, computer games, urban engineering, and behavioral science. One of the key capabilities agents should have is navigation, which is reaching goals without colliding with other agents or obstacles. The key challenge here is that the environment changes dynamically, where the current decision of an agent can largely affect the state of other agents as well as the agent in the future. Recently, reinforcement learning with deep neural networks has shown remarkable results in sequential decision-making problems. With the power of convolution neural networks, elaborate control with visual sensory inputs has also become possible. In this paper, we present an agent-based deep reinforcement learning approach for navigation, where only a simple reward function enables agents to navigate in various complex scenarios. Our method is also able to do that with a single unified policy for every scenario, where the scenario-specific parameter tuning is unnecessary. We will show the effectiveness of our method through a variety of scenarios and settings.