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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.
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.
Read full paper: https://arxiv.org/abs/1611.02779
Tags: Artificial Intelligence, Reinforcement Learning, Deep Learning
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By Arjun SrivastavaThe 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.
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.
Read full paper: https://arxiv.org/abs/1611.02779
Tags: Artificial Intelligence, Reinforcement Learning, Deep Learning