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#114 - Secrets of Deep Reinforcement Learning (Minqi Jiang)

Machine Learning Street Talk (MLST)

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Exploring Grounding in Deep Reinforcement Learning

This chapter investigates the intricate challenges of grounding and exploration in deep reinforcement learning, emphasizing the impact of training environments on agent performance. It covers the importance of aligning simulated experiences with real-world applications, discussing techniques like unsupervised environment design and curriculum learning to optimize training. The conversation also highlights the significance of Markov Decision Processes (MDPs) and how generative models can enhance the adaptability and performance of agents across diverse tasks.

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