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Universal reinforcement learning algorithms: Survey and experiments

Aslanides, John; Leike, Jan; Hutter, Marcus


Many state-of-the-art reinforcement learning (RL) algorithms typically assume that the environment is an ergodic Markov Decision Process (MDP). In contrast, the field of universal reinforcement learning (URL) is concerned with algorithms that make as few assumptions as possible about the environment. The universal Bayesian agent AIXI and a family of related URL algorithms have been developed in this setting. While numerous theoretical optimality results have been proven for these agents, there...[Show more]

CollectionsANU Research Publications
Date published: 2017
Type: Conference paper
Source: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
DOI: 10.24963/ijcai.2017/194
Access Rights: Open Access


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