Meta Reinforcement Learning
Read OriginalThis technical article delves into meta reinforcement learning (Meta-RL), explaining how agents can be trained to generalize and adapt rapidly to new reinforcement learning tasks. It traces the concept's origins from a 2001 paper to modern implementations like RL^2, discussing the training over distributions of MDPs and the goal of creating general-purpose learning algorithms.
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