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Eva Raggini (Machine Learning Programmer, Ubisoft)
Location: Room 3014, West Hall
Date: Wednesday, March 22
Time: 3:30 pm - 4:30 pm
Pass Type: All Access Pass, Core Pass
Topic: Design, Programming
Format: Lecture
Vault Recording: Video
Audience Level: Advanced
Sports video games like Roller Champions usually require more-advanced AI teammates with real-time strategic collaboration, and more complex and realistic agent interactions. In this context, it is essential to elaborate on less predictable behaviors closer to human actions to keep the player's interest and immersion.
This talk aims to unveil the multiple challenges facing the introduction of deep reinforcement learning (Deep RL) in a real-world game production that must deliver high-end results in a multi-player environment where agents must effectively collaborate with real players.
Eva Raggini, Machine Learning Programmer at Ubisoft, will show how the team achieved the modeling of multiple complex behaviors using multi-agent reinforcement learning (MARL), how they overcame a range of difficulties within complex training environments, their approach to handling collaboration between agents and players, and why they incorporated changes in the production workflow to rely more heavily on machine learning inference.
Attendees will gain useful insights into managing specific reinforcement learning issues in a multi-agent system environment. The goal is to help future machine learning programmers identify the challenges related to this technology, enlightening the workflow steps to model complex NPC behaviors required to achieve a production-ready quality.
This is for ML/AI programmers interested in reinforcement learning usage and technological advancement in game production.