A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidance

TL;DR

This paper introduces two novel exploration techniques for deep reinforcement learning to enhance UAV obstacle avoidance, demonstrating significant performance improvements in simulated environments.

Contribution

It proposes convergence-based and guidance-based exploration methods using a Domain Network, improving UAV obstacle avoidance in deep reinforcement learning.

Findings

Two-fold increase in average rewards over existing methods

Effective exploration in sparse reward environments

Validated in multiple 3-D simulation environments

Abstract

Integration of reinforcement learning with unmanned aerial vehicles (UAVs) to achieve autonomous flight has been an active research area in recent years. An important part focuses on obstacle detection and avoidance for UAVs navigating through an environment. Exploration in an unseen environment can be tackled with Deep Q-Network (DQN). However, value exploration with uniform sampling of actions may lead to redundant states, where often the environments inherently bear sparse rewards. To resolve this, we present two techniques for improving exploration for UAV obstacle avoidance. The first is a convergence-based approach that uses convergence error to iterate through unexplored actions and temporal threshold to balance exploration and exploitation. The second is a guidance-based approach using a Domain Network which uses a Gaussian mixture distribution to compare previously seen states to a predicted next state in order to select the next action. Performance and evaluation of these approaches were implemented in multiple 3-D simulation environments, with variation in complexity. The proposed approach demonstrates a two-fold improvement in average rewards compared to state of the art.