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Aerial observation is usually affected by the Earth’s atmosphere, especially when haze exists. Deep reinforcement learning was used in this study for dehazing. We first developed a clear–hazy aerial image dataset addressing various types of ground; we then compared the dehazing results of some state-of-the-art methods, including the classic dark channel prior, color attenuation prior, non-local image dehazing, multi-scale convolutional neural networks, DehazeNet, and all-in-one dehazing network. We extended the most suitable method, DehazeNet, to a multi-scale form and added it into a multi-agent deep reinforcement learning network called DRL_Dehaze. DRL_Dehaze was tested on several ground types and in situations with multiple haze scales. The results show that each pixel agent can automatically select the most suitable method in multi-scale haze situations and can produce a good dehazing result. Different ground scenes may best be processed using different steps.