Before revision: We present an approach for procedural content generation (PCG), and improving generalization in reinforcement learning (RL) agents, by using adversarial deep RL . Training RL agents for generalization over novel environments is a notoriously difficult task.
After revision: We present a new approach ARLPCG: Adversarial Reinforcement Learning for Procedural Content Generation, which procedurally generates and tests previously unseen environments with an auxiliary input as a control variable . Training RL agents for generalization over novel environments is a notoriously difficult task.