Learning good representations for and with reinforcement learning

人工智能（AI）在隔离智能的不同方面，提出灵活的表示和强大的算法，从而在特定任务中获得能力方面取得了巨大进展。例如，人工智能代理比人类更擅长围棋等游戏，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、强健和自主的能力，仍然是难以捉摸的。最好的人工智能系统仍然是针对特定问题进行调整的。我们的主要研究目标是开发通用的人工智能方法，其核心是依赖于强化学习。强化学习是一种从与环境的互动中学习的方法，受动物学习理论的启发。该提案旨在设计能够自动为强化学习代理创建表示的算法，使它们能够建模世界并在多个时间尺度上行动。我们的目标是提供新的优化标准，正式描述什么是一组好的抽象表示，提供基于梯度的学习算法来学习这些模型，并通过模拟领域，游戏以及实时序列预测数据集的经验评估来证明它们的有效性。我们将通过解释智能体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、循环神经网络的训练。

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.