2018/10/24 by Aaron David Tucker, Aaron Tucker, Adam Gleave +4 · 1 citation
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #FOS: Computer and information sciences #Human Pose and Action Recognition #I.2.6 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reinforcement Learning in Robotics #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1810.10593
10 pages, 4 figures. Submitted to NIPS Deep RL Workshop
arxiv created 2018/10/24 · openalex publication_date 2018/10/24 · arxiv updated 2018/10/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Deep reinforcement learning achieves superhuman performance in a range of video game environments, but requires that a designer manually specify a reward function. It is often easier to provide demonstrations of a target behavior than to design a reward function describing that behavior. Inverse reinforcement learning (IRL) algorithms can infer a reward from demonstrations in low-dimensional continuous control environments, but there has been little work on applying IRL to high-dimensional video games. In our CNN-AIRL baseline, we modify the state-of-the-art adversarial IRL (AIRL) algorithm to use CNNs for the generator and discriminator. To stabilize training, we normalize the reward and increase the size of the discriminator training dataset. We additionally learn a low-dimensional state representation using a novel autoencoder architecture tuned for video game environments. This embedding is used as input to the reward network, improving the sample efficiency of expert demonstrations. Our method achieves high-level performance on the simple Catcher video game, substantially outperforming the CNN-AIRL baseline. We also score points on the Enduro Atari racing game, but do not match expert performance, highlighting the need for further work.