强化学习(RL)是机器学习的一个领域,与软件代理应如何在环境中采取行动以最大化累积奖励的概念有关。除了监督学习和非监督学习外,强化学习是三种基本的机器学习范式之一。 强化学习与监督学习的不同之处在于,不需要呈现带标签的输入/输出对,也不需要显式纠正次优动作。相反,重点是在探索(未知领域)和利用(当前知识)之间找到平衡。 该环境通常以马尔可夫决策过程(MDP)的形式陈述,因为针对这种情况的许多强化学习算法都使用动态编程技术。经典动态规划方法和强化学习算法之间的主要区别在于,后者不假设MDP的确切数学模型,并且针对无法采用精确方法的大型MDP。

Algorithmic Information Theory has inspired intractable constructions of general intelligence (AGI), and undiscovered tractable approximations are likely feasible. Reinforcement Learning (RL), the dominant paradigm by which an agent might learn to solve arbitrary solvable problems, gives an agent a dangerous incentive: to gain arbitrary "power" in order to intervene in the provision of their own reward. We review the arguments that generally intelligent algorithmic-information-theoretic reinforcement learners such as Hutter's (2005) AIXI would seek arbitrary power, including over us. Then, using an information-theoretic exploration schedule, and a setup inspired by causal influence theory, we present a variant of AIXI which learns to not seek arbitrary power; we call it "unambitious". We show that our agent learns to accrue reward at least as well as a human mentor, while relying on that mentor with diminishing probability. And given a formal assumption that we probe empirically, we show that eventually, the agent's world-model incorporates the following true fact: intervening in the "outside world" will have no effect on reward acquisition; hence, it has no incentive to shape the outside world.

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