The Number One Educational Resource for the Game Industry
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Overview:
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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.
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