Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning

Enabling the capability of assessing risk and making risk-aware decisions is essential to applying reinforcement learning to safety-critical robots like drones. In this paper, we investigate a specific case where a nano quadcopter robot learns to navigate an apriori-unknown cluttered environment under partial observability. We present a...

HER-PDQN: A Reinforcement Learning Approach for UAV Navigation with Hybrid Action Spaces and Sparse Rewards

Reinforcement learning (RL) equipped with neural networks has recently led to a wide range of successes in learning policies for unmanned aerial vehicle (UAV) navigation and control problems. The success of RL relies on two human-designed heuristics: appropriate action space definition and reward function engineering. The commonly used fully...