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Outrider deploys reinforcement learning AI to enhance distribution yard throughput

đź“… Date:

đź”– Topics: reinforcement learning, Autonomous Vehicle

🏢 Organizations: Outrider, NVIDIA, Equinix


Outrider, the leader in autonomous yard operations for logistics hubs, announces its industry-first deployment of advanced reinforcement learning (RL) techniques to maximize freight throughput at customer sites. Outrider’s RL models increase path planning speed by 10x and enable the Outrider System to move freight more efficiently and safely through busy, complex distribution yards.

RL techniques involve creating a model that improves decision-making over time. Using years of data samples of behaviors, Outrider developed an RL curriculum of increasing difficulty for the model to learn. This technique reinforces preferred behaviors, such as following traffic rules and maintaining safe distances from other vehicles, and discourages undesirable behaviors. Once the RL models are tested extensively in simulation and on-vehicle at Outrider’s Advanced Testing Facility, the model and code are deployed into autonomous operations at customer sites.

Processing these data points through DL and RL models requires sophisticated computing hardware and a cost-effective hybrid cloud training environment that leverages public and private AI clouds. Outrider’s private AI cloud deployment utilizes NVIDIA DGX H200 GPUs installed at a secure, Denver-based data center owned and operated by Equinix.

Read more at Outrider