Are you looking for ways to improve the efficiency and success of your multi-agent reinforcement learning (MARL) experiments? Ray can automate MARL tuning by dynamically tracing and optimizing experiments across parameter spaces, while utilizing your computational resources via its tools such as AIR, Tune, and RLLib.
Combined with Weights & Biases, MARL engineers get a comprehensive view of their experiments, centralizing all details and assets into one ML system of record. This enables them to save time on experiment iterations and quickly determine which tunings produce optimal performance outcomes, providing faster results with fewer hiccups.
To demonstrate this, we will perform experiments on two scenarios: Autonomous Vehicle Driving and Drone Flying.
Anish loves turning ML ideas into ML products. He began his career working with several Data Science teams at SAP, utilizing traditional Machine Learning, Deep Learning, and building recommendation systems. Now, he is at Weights & Biases, engaging with practitioners to create the right tools, lessons, and collateral to make Machine Learning accessible to everyone. With the art of programming and a little bit of magic, Anish crafts ML projects to help better serve others, turning "oh no's" into "a-ha's"!
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