Character Animation Using Reinforcement Learning and Imitation Learning Algorithms

Real-time character animation for gaming and film industries is challenging and achieving production-ready quality requires a huge amount of time and resources. Animation through marker-based motion capture is quite a tiresome process that requires costly motion-capture suits, multiple cameras, and a large database. In this paper, we propose a model that aims to generate real-time character animation for biped locomotion in Unity ML(Machine Learning) agents using RL(Reinforcement learning) and IL(Imitation learning) algorithms. We first evaluate the training process with solely the state-of-the-art RL algorithm, PPO(Proximal Policy Optimization). Then we analyze the combination of IL algorithms BC(Behavioral Cloning) and GAIL(Generative Adversarial Imitation Learning) in conjunction with PPO. We further discuss the comparison between the two training results and show that our model can generate animations in real-time avoiding all the tedious work and large databases. We demonstrate that our approach is effortlessly easy to implement while maintaining the quality of the animation.