I’m working on a Q-learning project using OpenAI Gym and PyBullet drones. My goal is to control the height of the drone so that it stays at a height of 1 and remains stable at that point. I’m using discrete actions 0, 1, and 2, which correspond to setting the drone’s altitude to [0 0 0 0], [1 1 1 1], and [-1 -1 -1 -1] respectively. Initially, I tried setting the reward as ‘reward = (1 - (next_state))**2‘, but I noticed that the reward and the drone’s altitude were inversely proportional, meaning that as the drone descended, the reward increased. When I didn’t add any reward function, the drone stayed at a height of 1.5 instead of 1.