Can we build a generalist agent? Dr. Minqi Jiang and Dr. Marc Rigter
Machine Learning Street Talk (MLST)
00:00
World Modeling in Reinforcement Learning
This chapter explores the importance of world modeling in reinforcement learning, arguing it enhances sample efficiency and decision-making. The speakers discuss challenges related to data sparsity and the complexities of training robust models, introducing concepts like domain randomization and adversarial sampling. Through the lens of latent space and partially observable states, they highlight how learning and generalizing from foundational skills can lead to more optimal strategies in various environments.
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