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A multi-objective deep reinforcement learning framework
یک چارچوب یادگیری تقویتی عمیق چند هدفه-2020 This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework
based on deep Q-networks. We develop a high-performance MODRL framework that supports both singlepolicy
and multi-policy strategies, as well as both linear and non-linear approaches to action selection. The
experimental results on two benchmark problems (two-objective deep sea treasure environment and threeobjective
Mountain Car problem) indicate that the proposed framework is able to find the Pareto-optimal
solutions effectively. The proposed framework is generic and highly modularized, which allows the integration
of different deep reinforcement learning algorithms in different complex problem domains. This therefore
overcomes many disadvantages involved with standard multi-objective reinforcement learning methods in the
current literature. The proposed framework acts as a testbed platform that accelerates the development of
MODRL for solving increasingly complicated multi-objective problems. Keywords: Reinforcement learning | Multi-objective | Deep learning | Single-policy | Multi-policy |
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