Multi-objective Reinforcement Learning through Continuous Pareto Manifold Approximation
نویسندگان
چکیده
منابع مشابه
Multi-objective Reinforcement Learning through Continuous Pareto Manifold Approximation
Many real-world control applications, from economics to robotics, are characterized by the presence of multiple conflicting objectives. In these problems, the standard concept of optimality is replaced by Pareto–optimality and the goal is to find the Pareto frontier, a set of solutions representing different compromises among the objectives. Despite recent advances in multi–objective optimizati...
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This paper is about learning a continuous approximation of the Pareto frontier in Multi–Objective Markov Decision Problems (MOMDPs). We propose a policy–based approach that exploits gradient information to generate solutions close to the Pareto ones. Differently from previous policy–gradient multi–objective algorithms, where n optimization routines are use to have n solutions, our approach perf...
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Many real-world problems involve the optimization of multiple, possibly conflicting objectives. Multi-objective reinforcement learning (MORL) is a generalization of standard reinforcement learning where the scalar reward signal is extended to multiple feedback signals, in essence, one for each objective. MORL is the process of learning policies that optimize multiple criteria simultaneously. In...
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ژورنال
عنوان ژورنال: Journal of Artificial Intelligence Research
سال: 2016
ISSN: 1076-9757
DOI: 10.1613/jair.4961