The availability of diverse data has increased the demand for expertise in algorithmic trading strategies. Reinforcement learning has shown interesting applicability in a wide range of tasks, especially in some challenging problems as trading, where slow model convergence, inference speed, and reduced model accuracy appear as barriers in this type of application. In this paper, we propose the transformation of time series into images considering a transfer learning based on a semi-supervised

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... el with deep Q learning agents, where labels were generated by an evolutionary algorithm to improve both training speed and performance measures.