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