Drones with obstacle avoidance capabilities have attracted much attention from researchers recently. They typically adopt either supervised learning or reinforcement learning (RL) for training their networks. The drawback of supervised learning is that labeling of the massive dataset is laborious and time-consuming, whereas RL aims to overcome such a problem by letting an agent learn with the data from its environment. The present study aims to utilize diverse RL within two categories: (1) discrete action space and (2) continuous action space. The former has the advantage in optimization for vision datasets, but such actions can lead to unnatural behavior. For the latter, we propose a U-net based segmentation model with an actor-critic network. Performance is compared between these RL algorithms with three different environments such as the woodland, block world, and the arena world, as well as racing with human pilots. Results suggest that our best continuous algorithm easily outperformed the discrete ones and yet was similar to an expert pilot.