[2006.16712] Model-based Reinforcement Learning: A Surveyopen searchopen navigation menucontact arXivsubscribe to arXiv mailings

Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is a key challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This paper presents a survey of the integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main steps. First, we systematically cover approaches to dynamics model learning, including challenges like dealing with stochasticity, uncertainty, partial observability, and temporal abstraction. Second, we present a systematic categorization of planning-learning integration, including aspects like: where to start planning, what budgets to allocate to planning and real data collection, how to plan, and how to integrate planning in the learning and acting loop. After these two key sections, we also discuss the potential benefits of model-based RL, like enhanced data efficiency, targeted exploration, and improved stability. Along

2 mentions: @dennybritz@helioRocha_
Date: 2020/07/01 08:21

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@dennybritz Model-based Reinforcement Learning: A Survey: t.co/5tvG5Npx4v “A systematic categorization of planning-learning integration, including where to start planning, what budgets to allocate, how to plan, and how to integrate planning in the learning and acting loop” t.co/m1DEgdeiU8

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