RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning

The paper delves into the problem of slow learning in deep reinforcement learning compared to human and animal learning speeds. It introduces RL2, an innovative approach that uses meta-learning to train a recurrent neural network (RNN) to learn a fast RL algorithm efficiently.

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AI-generated voices discussing a paper selected by Arjun Srivastava.

About this paper

Engineers and specialists can benefit from RL2 by understanding how meta-learning can bridge the gap between slow deep reinforcement learning and fast human learning speeds. This approach offers a way to encode prior knowledge in an RNN to make RL algorithms more efficient, adaptable, and scalable to complex real-world scenarios.

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