Improving Generalization in Game Agents with Data Augmentation in Imitation Learning

Imitation learning is an effective approach for training game-playing agents and, consequently, for efficient game production. However, generalization - the ability to perform well in related but unseen scenarios - is an essential requirement that remains an unsolved challenge for game AI. Generalization is difficult for imitation learning agents because it requires the algorithm to take meaningful actions outside of the training distribution. In this paper we propose a solution to this challenge. Inspired by the success of data augmentation in supervised learning, we augment the training data so the distribution of states and actions in the dataset better represents the real state-action distribution. This study evaluates methods for combining and applying data augmentations to observations, to improve generalization of imitation learning agents. It also provides a performance benchmark of these augmentations across several 3D environments. These results demonstrate that data augmentation is a promising framework for improving generalization in imitation learning agents.

Generating Personas for Games with Multimodal Adversarial Imitation Learning

Reinforcement learning has been widely successful in producing agents capable of playing games at a human level. However, this requires complex reward engineering, and the agent's resulting policy is often unpredictable. Going beyond reinforcement learning is necessary to model a wide range of human playstyles, which can be difficult to represent with a reward function. This paper presents a novel imitation learning approach to generate multiple persona policies for playtesting. Multimodal Generative Adversarial Imitation Learning (MultiGAIL) uses an auxiliary input parameter to learn distinct personas using a single-agent model. MultiGAIL is based on generative adversarial imitation learning and uses multiple discriminators as reward models, inferring the environment reward by comparing the agent and distinct expert policies. The reward from each discriminator is weighted according to the auxiliary input. Our experimental analysis demonstrates the effectiveness of our technique in two environments with continuous and discrete action spaces.

Automatic Testing and Validation of Level of Detail Reductions Through Supervised Learning

Modern video games are rapidly growing in size and scale, and to create rich and interesting environments, a large amount of content is needed. As a consequence, often several thousands of detailed 3D assets are used to create a single scene. As each asset's polygon mesh can contain millions of polygons, the number of polygons that need to be drawn every frame may exceed several billions. Therefore, the computational resources often limit how many detailed objects that can be displayed in a scene. To push this limit and to optimize performance one can reduce the polygon count of the assets when possible. Basically, the idea is that an object at farther distance from the capturing camera, consequently with relatively smaller screen size, its polygon count may be reduced without affecting the perceived quality. Level of Detail (LOD) refers to the complexity level of a 3D model representation. The process of removing complexity is often called LOD reduction and can be done automatically with an algorithm or by hand by artists. However, this process may lead to deterioration of the visual quality if the different LODs differ significantly, or if LOD reduction transition is not seamless. Today the validation of these results is mainly done manually requiring an expert to visually inspect the results. However, this process is slow, mundane, and therefore prone to error. Herein we propose a method to automate this process based on the use of deep convolutional networks. We report promising results and envision that this method can be used to automate the process of LOD reduction testing and validation.

Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning

In games, as in and many other domains, design validation and testing is a huge challenge as systems are growing in size and manual testing is becoming infeasible. This paper proposes a new approach to automated game validation and testing. Our method leverages a data-driven imitation learning technique, which requires little effort and time and no knowledge of machine learning or programming, that designers can use to efficiently train game testing agents. We investigate the validity of our approach through a user study with industry experts. The survey results show that our method is indeed a valid approach to game validation and that data-driven programming would be a useful aid to reducing effort and increasing quality of modern playtesting. The survey also highlights several open challenges. With the help of the most recent literature, we analyze the identified challenges and propose future research directions suitable for supporting and maximizing the utility of our approach.

CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories

This paper proposes a novel deep reinforcement learning algorithm to perform automatic analysis and detection of gameplay issues in complex 3D navigation environments. The Curiosity-Conditioned Proximal Trajectories (CCPT) method combines curiosity and imitation learning to train agents to methodically explore in the proximity of known trajectories derived from expert demonstrations. We show how CCPT can explore complex environments, discover gameplay issues and design oversights in the process, and recognize and highlight them directly to game designers. We further demonstrate the effectiveness of the algorithm in a novel 3D navigation environment which reflects the complexity of modern AAA video games. Our results show a higher level of coverage and bug discovery than baselines methods, and it hence can provide a valuable tool for game designers to identify issues in game design automatically.

Augmenting Automated Game Testing with Deep Reinforcement Learning

General game testing relies on the use of human play testers, play test scripting, and prior knowledge of areas of interest to produce relevant test data. Using deep reinforcement learning (DRL), we introduce a self-learning mechanism to the game testing framework. With DRL, the framework is capable of exploring and/or exploiting the game mechanics based on a user-defined, reinforcing reward signal. As a result, test coverage is increased and unintended game play mechanics, exploits and bugs are discovered in a multitude of game types. In this paper, we show that DRL can be used to increase test coverage, find exploits, test map difficulty, and to detect common problems that arise in the testing of first-person shooter (FPS) games.

Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents

As modern games continue growing both in size and complexity, it has become more challenging to ensure that all the relevant content is tested and that any potential issue is properly identified and fixed. Attempting to maximize testing coverage using only human participants, however, results in a tedious and hard to orchestrate process which normally slows down the development cycle. Complementing playtesting via autonomous agents has shown great promise accelerating and simplifying this process. This paper addresses the problem of automatically exploring and testing a given scenario using reinforcement learning agents trained to maximize game state coverage. Each of these agents is rewarded based on the novelty of its actions, thus encouraging a curious and exploratory behaviour on a complex 3D scenario where previously proposed exploration techniques perform poorly. The curious agents are able to learn the complex navigation mechanics required to reach the different areas around the map, thus providing the necessary data to identify potential issues. Moreover, the paper also explores different visualization strategies and evaluates how to make better use of the collected data to drive design decisions and to recognize possible problems and oversights.

Adversarial Reinforcement Learning for Procedural Content Generation

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. One popular approach is to procedurally generate different environments to increase the generalizability of the trained agents. Here we deploy an adversarial model with one PCG RL agent (called Generator), and one solving RL agent (called Solver). The benefit is mainly two-fold: Firstly, the Solver achieves better generalization through the generated challenges from the Generator. Secondly, the trained Generator can be used as a creator of novel environments that, together with the Solver, can be shown to be solvable. The Generator receives a reward signal based on the performance of the Solver which encourages the environment design to be challenging but not impossible. To further drive diversity and control of the environment generation, we propose the use of auxiliary inputs for the Generator. Thus, we propose adversarial RL for procedural content generation (ARLPCG), an adversarial approach which procedurally generates previously unseen environments with an auxiliary input as a control variable. Herein we describe this concept in detail and compare it with previous methods showing improved generalization, as well as a new method to create novel environments.