Uploaded December 2024 | Updated September 2026, 2 weeks ago
Title: GTSophy: Outracing champion Gran Turismo drivers with deep reinforcement learning
Speaker: Ishan Durugkar (Research Scientist at Sony AI)
Date: Friday, October 30, 2024
Abstract: Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres to pass or block opponents while operating their vehicles at their traction limits. Racing simulations, such as the PlayStation game Gran Turismo, faithfully reproduce the non-linear control challenges of real race cars while also encapsulating the complex multi-agent interactions. This talk describes how we trained agents for Gran Turismo that can compete with the world's best e-sports drivers. This agent, Gran Turismo Sophy, was evaluated in a head-to-head competition against four of the world's best Gran Turismo drivers and won. Thereafter, this agent was brought to production, with GT Sophy now available for all players to play on multiple tracks, across hundreds of cars, on their personal PlayStation 5. I will go over this journey and some of the challenges ahead.
Biography: Ishan Durugkar is a research scientist at Sony AI working with the Game AI team to bring reinforcement learning agents to games such as Gran Turismo. He completed his PhD from UT Austin, where he focused on reinforcement learning, robotics, and multi-agent systems. Most notably Ishan has used distribution matching techniques for goal-conditioned reinforcement learning, unsupervised skill discovery, sim-to-real policy transfer, and multi-agent coordination.
This video is closed captioned.
Title: GTSophy: Outracing champion Gran Turismo drivers with deep reinforcement learning
Speaker: Ishan Durugkar (Research Scientist at Sony AI)
Date: Friday, October 30, 2024
Abstract: Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres to pass or block opponents while operating their vehicles at their traction limits. Racing simulations, such as the PlayStation game Gran Turismo, faithfully reproduce the non-linear control challenges of real race cars while also encapsulating the complex multi-agent interactions. This talk describes how we trained agents for Gran Turismo that can compete with the world's best e-sports drivers. This agent, Gran Turismo Sophy, was evaluated in a head-to-head competition against four of the world's best Gran Turismo drivers and won. Thereafter, this agent was brought to production, with GT Sophy now available for all players to play on multiple tracks, across hundreds of cars, on their personal PlayStation 5. I will go over this journey and some of the challenges ahead.
Biography: Ishan Durugkar is a research scientist at Sony AI working with the Game AI team to bring reinforcement learning agents to games such as Gran Turismo. He completed his PhD from UT Austin, where he focused on reinforcement learning, robotics, and multi-agent systems. Most notably Ishan has used distribution matching techniques for goal-conditioned reinforcement learning, unsupervised skill discovery, sim-to-real policy transfer, and multi-agent coordination.
This video is closed captioned.
![[Audio Descriptions] Faculty In Focus: Nirvan Tyagi](https://i.ytimg.com/vi/v8NzVoue-Ac/mqdefault.jpg "[Audio Descriptions] Faculty In Focus: Nirvan Tyagi
In this episode of the Allen School’s “Faculty in Focus” series, Assistant Professor Nirvan Tyagi discusses his work on cryptography techniques such as zero knowledge proofs, which would enable systems used in banking and other services to provide strong privacy for users while protecting them against bad actors. His research aims to give technology users more control over their data while maintaining efficiency and performance.
For a version without audio descriptions, visit https://youtu.be/PMaUVhjEJlA.")
