UC Berkeley Researchers Introduce SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning

In recent years, researchers in the field of robotic reinforcement learning (RL) have achieved significant progress, developing methods capable of handling complex image observations, training in real-world scenarios, and incorporating auxiliary data, such as demonstrations and prior experience. Despite these advancements, practitioners acknowledge the inherent difficulty in effectively utilizing robotic RL, emphasizing that the specific […]

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