Improving Generalization in Game AI Agents through Data Augmentation in Imitation Learning

Core Concepts

Data augmentation can improve the generalization performance of imitation learning agents in game environments.

Abstract

The paper investigates the use of data augmentation techniques to improve the generalization of imitation learning (IL) agents in game environments. The authors first collect a set of demonstrations using a training environment, where an agent must navigate to a building, press a button to open a door, and enter the goal position. They then train IL agents using the original dataset as well as augmented datasets, applying various combinations of data augmentations such as Gaussian noise, uniform noise, scaling, state mixup, continuous dropout, and semantic dropout.

The authors evaluate the trained agents in four distinct test environments, which differ from the training environment in terms of goal position, obstacle placement, and overall complexity. The results show that certain combinations of data augmentations can significantly improve the generalization performance of the IL agents, with the best-performing models achieving a 60% improvement over the baseline non-augmented model. The authors identify scaling, state mixup, and continuous dropout as the most consistently effective augmentations across the test environments.

The paper provides valuable insights into the use of data augmentation to address the generalization challenge in game AI, particularly for imitation learning agents. The comprehensive evaluation across multiple test environments demonstrates the potential of this approach to enhance the robustness and adaptability of game-playing agents.

Stats

The paper does not provide any specific numerical data or statistics to support the key findings. The results are presented in the form of relative performance comparisons between the augmented models and the baseline non-augmented model.

Quotes

The paper does not contain any direct quotes that are particularly striking or support the key arguments.

Key Insights Distilled From

Improving Generalization in Game Agents with Data Augmentation in Imitation Learning

by Dere... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2309.12815.pdf

Deeper Inquiries

What other types of data augmentation techniques, beyond the ones explored in this study, could be investigated to further improve the generalization of imitation learning agents in game environments

In addition to the data augmentation techniques explored in the study, several other methods could be investigated to further enhance the generalization of imitation learning agents in game environments. One potential approach is mixup, a technique that combines pairs of examples during training to create new synthetic examples. By blending the features and labels of two instances, mixup encourages the model to learn a more generalized decision boundary, reducing overfitting and improving robustness. Another technique is cutout, where random sections of input data are masked out during training. This method forces the model to focus on the relevant features and prevents it from relying too heavily on specific patterns in the data. Random erasing is another variation of this concept, where random patches of input data are replaced with noise or random values, encouraging the model to learn from the remaining information.

How would the performance of the augmented imitation learning agents compare to agents trained using reinforcement learning with domain randomization or other generalization techniques

The performance of augmented imitation learning agents could be compared to agents trained using reinforcement learning with domain randomization or other generalization techniques. Reinforcement learning with domain randomization involves training agents in a variety of simulated environments with randomized factors such as textures, lighting conditions, or object placements. This approach aims to expose the agent to a diverse range of scenarios to improve generalization. While reinforcement learning with domain randomization can be effective, it often requires significant computational resources and time for training. In contrast, data augmentation offers a more efficient way to enhance generalization by manipulating the existing training data. By augmenting the dataset with various transformations, imitation learning agents can learn to adapt to unseen scenarios without the need for extensive training in diverse environments.

Could the insights and findings from this study be extended to other types of game AI tasks, such as strategy games or first-person shooters, or are the results specific to the navigation and interaction task explored in this paper

The insights and findings from this study could be extended to other types of game AI tasks, such as strategy games or first-person shooters, with some considerations. While the specific augmentations and their impact on generalization may vary depending on the task and environment, the underlying principle of using data augmentation to improve generalization remains applicable. For strategy games, where decision-making and long-term planning are crucial, augmentations that enhance the agent's ability to learn from diverse scenarios could be particularly beneficial. In first-person shooters, where quick reactions and spatial awareness are key, augmentations that improve the agent's understanding of the game environment and its interactions could be more relevant. By adapting the data augmentation techniques explored in this study to different game genres and tasks, researchers can enhance the generalization capabilities of imitation learning agents across a wide range of game AI applications.