Reinforcement learning offers a promising methodology for developing skills for simulated characters, but typically requires working with sparse hand-crafted features. Building on recent progress in deep reinforcement learning (DeepRL), we introduce a mixture of actor-critic experts (MACE) approach that learns terrain-adaptive dynamic locomotion skills using high-dimensional state and terrain descriptions as input, and parameterized leaps or steps as output actions. MACE learns more quickly than a single actor-critic approach and results in actor-critic experts that exhibit specialization. Additional elements of our solution that contribute towards efficient learning include Boltzmann exploration and the use of initial actor biases to encourage specialization. Results are demonstrated for multiple planar characters and terrain classes.

The proposed method robustly and efficiently computes a navigation mesh for arbitrary and dynamic 3D environments based on curvature. This method addresses a number of known limitations in state-of-the-art techniques to produce navigation meshes that are tightly coupled to the original geometry, incorporate geometric details that are crucial for movement decisions and can robustly handle complex surfaces. The method is integrated into a standard navigation and collision-avoidance system to simulate thousands of agents on complex 3D surfaces in real-time.

Computational approaches for crowd analysis and environment design need robust measures for characterizing the relation between environments and crowd flow. Level of service (Level of Service) is a standard indicator for characterizing the service afforded by environments to crowds of specific densities, and is widely used in crowd management and urban design. However, current Level of Service indicators are qualitative and rely on expert analysis. In this paper, we perform a systematic analysis of Level of Service for synthetic crowds. The flow-density relationships in crowd evacuation scenarios are explored with respect to three state-of-the-art steering algorithms. Our results reveal that Level of Service is sensitive to the crowd behavior, and the configuration of the environment benchmark. Following this study, we automatically optimize environment elements to maximize crowd flow, under a range of Level of Service conditions. The steering algorithm, the number of optimized environment elements, the scenario configuration and the Level of Service conditions affect the optimal configuration of environment elements. We observe that the critical density of crowd simulators increases due to the optimal placement of pillars, thereby effectively increasing the Level of Service of environments at higher crowd densities. Depending on the simulation technique and environment benchmark, pillars are configured to produce lanes or form wall-like structures, in an effort to maximize crowd flow. These experiments serve as an important precursor to computational crowd optimization and management and motivate the need for further study using additional real and synthetic crowd datasets across a larger representation of environment benchmarks.

In this work we parameterized the configuration of a game level. With the parameterization we optimized aspects of the game level to change a players expected difficulty. Given specific constrains on the configuration of the game level we can produce game levels with a varying degree of difficulty.

In this work we parameterize subspaces of the full environment configuration space and optimize parts of these subspaces for an agent flow metric. We focus on building configuration and optimize the possible placement of support pillars or obstacles in the environment.