Adversarial reinforcement learning for procedural content generation and improved generalization

Abstract

Methods, apparatus and systems are provided for training a first reinforcement-learning (RL) agent and a second RL agent coupled to a computer game environment using RL techniques. The first RL agent iteratively generates a sub-goal sequence in relation to an overall goal within the computer game environment, where the first RL agent generates a new sub-goal for the sub-goal sequence after a second RL agent, interacting with the computer game environment, successfully achieves a current sub-goal in the sub-goal sequence. The second RL agent iteratively interacts with the computer game environment to achieve the current sub-goal in which each iterative interaction includes an attempt by the second RL agent for interacting with the computer game environment to achieve the current sub-goal. The first RL agent is updated using a first reward issued when the second RL agent successfully achieves the current sub-goal. The second RL agent is updated when a second reward is issued by the computer game environment based on the performance of the second RL agent attempting to achieve said current sub-goal. Once validly trained, the first RL agent forms a final first RL agent for automatic procedural content generation (PCG) in the computer game environment and the second RL agent forms a final second RL agent for automatically interacting with a PCG computer game environment.