Project Description
This Computer Animation class project utilized off-the-shelf Soft-Actor-Critic (SAC) Reinforcement Learning method to teach an animated character to build up momentum and swing on a pull-up bar.
The project demonstrates how modern RL algorithms can be applied to character animation tasks, creating realistic and dynamic movement patterns that emerge from the learning process rather than being hand-crafted.
Technical Approach
- • Soft Actor-Critic (SAC) reinforcement learning algorithm
- • Physics-based character simulation
- • Reward shaping for momentum building and swinging behavior
- • Real-time character animation from learned policies
- • Integration with physics simulation environment
Results
The trained agent successfully learned to generate realistic swinging motions, demonstrating the ability to: build up initial momentum, maintain rhythmic swinging patterns, and adapt to different starting conditions. The resulting animations showed natural-looking character movement that would be difficult to achieve through traditional keyframe animation techniques.