Reinforcement learning and genetic programming for motion acquisition
Adam Szarowicz, Jarosław Francik, Ewa Lach · Research Repository (Kingston University London) · 2005
While computer animation is currently widely used to create characters in games, films, and various other applications, techniques such as motion capture and keyframing are still relatively expensive. Automatic acquisition of secondary motion and/or motion prototyping using machine learning might be a solution to this problem. Our paper presents anapplication of two of the machine learning algorithms to generate action sequences for animated characters: Reinforcement Learning (RL) and Genetic Programming (GP). RL can be used in both deterministic and non-deterministic environments to generate actions which can later be incorporated into more complex animation sequences. The paper presents an application of both deterministic and non-deterministic updates of the Q-learning algorithm to automatic acquisition of motion. Results obtained from the learning system are also compared to human motion and conclusions are drawn. The second approach presented in this paper represents a virtual agent as an automaton trying to achieve its goal by executing an internal program. The agent's program is built using the layered learning genetic programming technique. The paper presents also an approach, in which a virtual character is treated as an automation executing some internal program so that to achieve some well defined goal. This program is created using layered learning genetic programming technique.