  Many existing obstacle avoidance algorithms overlook the crucial balance
between safety and agility, especially in environments of varying complexity.
In our study, we introduce an obstacle avoidance pipeline based on
reinforcement learning. This pipeline enables drones to adapt their flying
speed according to the environmental complexity. Moreover, to improve the
obstacle avoidance performance in cluttered environments, we propose a novel
latent space. The latent space in this representation is explicitly trained to
retain memory of previous depth map observations. Our findings confirm that
varying speed leads to a superior balance of success rate and agility in
cluttered environments. Additionally, our memory-augmented latent
representation outperforms the latent representation commonly used in
reinforcement learning. Finally, after minimal fine-tuning, we successfully
deployed our network on a real drone for enhanced obstacle avoidance.
