# Simultaneously Evolving Deep Reinforcement Learning Models via Multifactorial Optimization
## Abstract
In the recent years, Multifactorial Optimization (MFO) has attracted a lot of interest in the optimization community. MFO is known for its inherent skills to address multiple complex optimization tasks at the same time, while inter-task information transfer is used to improve their convergence speed. These skills make Multifactorial Evolution appealing to be applied to evolve Deep Reinforcement Learning (DQL) models, which is the scenario tackled in this paper. Complex DQL models usually find difficult to converge to optimal solutions, due to the lack of exploration or sparse rewards. In order to overcome these drawbacks, pre-trained models are commonly used to make Transfer Learning, transferring knowledge from the pre-trained to the target domain. Besides, it has been shown that the lack of exploration can be reduced by using meta-heuristic optimization approaches. In this paper we aim to explore the use of the MFO framework to optimize DQL models, making an analysis between MFO and the traditional Transfer Learning and metaheuristic approaches in terms of convergence, speed and policy quality.
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Source code of MFEA used in the paper: [link](https://github.com/HuangLingYu96/MFEA)
MFEA is able to evolve multiple scenarios with the codification proposed and good results are achieved. In scenarios like *Pendulum* it finds more difficult to converge and so, worst results are harvested.
Finally, the effectiveness of the crossovers is studied. The knowledge transference in MFEA is done via this mechanism, thus, it is relevant to check its effectiveness:
