Character Behavior Automation Using Deep Reinforcement Learning

Character Behavior Automation Using Deep Reinforcement Learning

Blog Article

Recently, various new attempts are being made to improve the quality of media content according to the expansion of the media market.Pre-visualization is one of those attempts, and the behavior of characters (agents) in virtual space is essential for pre-visualization.In this paper, serra avatar price a study was conducted to automatically generate behaviors of virtual characters for more efficient visualization in pre-visualization.

In particular, we propose a method to automatically produce an appropriate behavior by detecting the state of the surrounding environment with a deep reinforcement learning technique.A virtual environment is created using a game engine to configure space for reinforcement learning, and a reinforcement learning model of the training environment is configured with Python and PyTorch.The virtual environment and the model training environment are communicated with the ML-agents toolkit.

In the virtual environment, the character basically moves in a straight line, and three obstacles appear at random locations in front of the character.The character senses 9 states and allows 5 actions.After that, a reward is offered according to the action to proceed with learning.

For performance the gel bottle audrey evaluation, reinforcement learning training was conducted using the Proximal Policy Optimization (PPO) algorithm and Soft Actor-Critic (SAC) algorithm, and performance comparisons were also conducted according to the batch size.As a result, we are able to secure a reinforcement learning model with obstacle avoidance capability.Applying the model to the character proved that the character can automatically animate according to the state of the surrounding environment without explicit programming.