Learning good representations for and with reinforcement learning

人工智能（AI）在隔离智能的不同方面取得了巨大进展，并提出了灵活的表示和强大的算法，从而能够胜任特定任务。例如，AI智能体比人类更擅长玩围棋等游戏，这曾经被认为是不可能的。然而，人类甚至动物通常表现出的那种灵活、健壮和自主的能力仍然难以捉摸。最好的人工智能系统仍然是针对特定问题而调整的。我们的主要研究目标是开发通用的人工智能方法，其核心是强化学习。强化学习是一种从与环境的交互中学习的方法，受到动物学习理论的启发。该提案旨在设计可以自动为强化学习代理创建表示的算法，这些代理允许它们对世界进行建模并在多个时间尺度上采取行动。我们的目标是提供新的优化标准，正式描述什么是一个很好的一套抽象的表示，提供基于梯度的学习算法来学习这样的模型，并证明其有效性，通过经验评估模拟域，游戏，以及真实的时间序列预测数据集。 我们将通过解释智能体应如何在其环境中移动以优化其学习速度来解决探索的关键问题。最后，我们将在其他算法中利用这些方法，这些算法可以从多个时间尺度中受益，例如深度递归神经网络的训练。

Artificial intelligence (AI) has made great progress in isolating different aspects of intelligence and proposing flexible representations and powerful algorithms that lead to competence in specific tasks. For example, AI agents are better than humans at playing games like Go, a feat once considered impossible. However, the sort of flexible, robust, and autonomous competence routinely exhibited by humans, or even animals, remains elusive. The best AI systems are still tuned to specific problems. Our main research goal is to develop general AI methodology that relies, at its core, on reinforcement learning. Reinforcement learning is an approach to learning from interaction with an environment, inspired by animal learning theory. This proposal aims to design algorithms that can automatically create representations for reinforcement learning agents which allow them to model the world and to act at multiple time scales. We aim to provide new optimization criteria which describe formally what is a good set of abstract representations, provide gradient-based learning algorithms to learn such models, and demonstrate their effectiveness through empirical evaluations in simulated domains, game playing, as well as real time series prediction data sets. We will tackle the crucial problem of exploration, by explaining how an agent should move about its environment in order to optimize its learning speed. Finally, we will leverage these methods inside other algorithms that can benefit from multiple time scales, such as the training of deep, recurrent neural networks.