BayJarvis: Blogs on in-context-rl

paper AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents - 2024-02-26

In the realm of Reinforcement Learning (RL), the paper introduces AMAGO, an innovative in-context RL agent designed to tackle the challenges of generalization, long-term memory, and meta-learning. AMAGO utilizes sequence models, specifically Transformers, to learn from entire rollouts in parallel, marking a significant departure from traditional approaches that often require extensive tuning and face scalability issues. …