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Imitation Learning: Building Practical Agents to Test and Explore a First-Person Shooter

Igor Borovikov  (Sr. AI Scientist, EA)

Date: Tuesday, August 4

Time: 4:00pm - 4:30pm

Pass Type: Conference Pass - Get your pass now!

Topic: Programming

Format: Session

Vault Recording: Video

Audience Level: Intermediate

The talk introduces style-centric autoplay agents that game developers can train quickly and on a budget to facilitate testing and evaluating the gameplay of a title under development. The focus on the practical and stylistic aspects suggests a simple approach based on the Markov model. Markov agents are trainable interactively and can incorporate new game features without re-training the entire model. While such agents efficiently capture the demonstrated gameplay style, they may require fallback heuristics to address possible lack of performance, or the game states not explicitly present in organic play-throughs. Imitation learning on the data bootstrapped from the enhanced with heuristics Markov agent allows training of a compact computationally efficient DNN model suitable for automated game evaluation and testing. A generic First-Person Shooter game example provides a practical context for the presentation. The GitHub repository illustrates Markov agents in more detail and uses OpenAI gym environments for an interactive demo.


The attendees will get an insight into a simple yet effective approach to creating autoplay agents for game testing and evaluation during its development.

Intended Audience

The target audience is moderately experienced AI game developers with the basic knowledge of Markov processes, basic concepts of Imitation and Reinforcement Learning, Deep Neural Nets, interested in creating their autoplay bots for game testing and balancing.