Curiosity-Driven Reinforcement Learning based Low-Level Flight Control
Amir Ramezani Dooraki Alexandros Iosifidis
Curiosity is one of the main motives in many of the natural creatures with
measurable levels of intelligence for exploration and, as a result, more
efficient learning. It makes it possible for humans and many animals to explore
efficiently by searching for being in states that make them surprised with the
goal of learning more about what they do not know. As a result, while being
curious, they learn better. In the machine learning literature, curiosity is
mostly combined with reinforcement learning-based algorithms as an intrinsic
reward. This work proposes an algorithm based on the drive of curiosity for
autonomous learning to control by generating proper motor speeds from odometry
data. The quadcopter controlled by our proposed algorithm can pass through
obstacles while controlling the Yaw direction of the quad-copter toward the
desired location. To achieve that, we also propose a new curiosity approach
based on prediction error. We ran tests using on-policy, off-policy, on-policy
plus curiosity, and the proposed algorithm and visualized the effect of
curiosity in evolving exploration patterns. Results show the capability of the
proposed algorithm to learn optimal policy and maximize reward where other
algorithms fail to do so.