A Multi-Objective Deep Reinforcement Learning Framework
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This paper presents a new multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We propose linear and non-linear methods to develop the MODRL framework that includes both single-policy and multi-policy strategies. The experimental results on a deep sea treasure environment indicate that the proposed approach is able to converge to the optimal Pareto solutions. The proposed framework is generic, which allows implementation of different deep reinforcement learning algorithms in various complex environments. Details of the framework implementation can be referred to http://www.deakin.edu.au/ thanhthi/drl.htm.
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