In my earlier post on meta-learning, the problem is mainly defined in the context of few-shot classification. Here I would like to explore more into cases when we try to “meta-learn” Reinforcement Learning (RL) tasks by developing an agent that can solve unseen tasks fast and efficiently.
To recap, a good meta-learning model is expected to generalize to new tasks or new environments that have never been encountered during training. The adaptation process, essentially a mini learning session, happens at test with limited exposure to the new configurations.

Lilian Weng is a machine learning researcher and writer who shares insights, research findings, and tutorials on machine learning, artificial intelligence, and data science. Through articles, blog posts, and research summaries, Lilian Weng explores topics such as deep learning, natural language processing, and reinforcement learning. Readers can learn about state-of-the-art algorithms, practical applications of machine learning, and trends shaping the field of AI.

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A good meta-learning model is expected to generalize to new tasks or new environments. The adaptation process happens at test with limited exposure to the new configurations. Even without any explicit fine-tuning (no gradient backpropagation on trainable variables) the model autonomously adjusts internal hidden states to learn.

Meta Reinforcement Learning