Continuous control of an underground loader using deep reinforcement learning

TL;DR

This paper demonstrates the use of deep reinforcement learning with multi-agent neural networks to control an underground loader in simulation, optimizing for efficiency and adaptability in complex environments.

Contribution

It introduces a multi-agent deep reinforcement learning approach for continuous underground loader control, integrating perception and action for improved operational efficiency.

Findings

Achieved 75% of maximum bucket capacity on average.

Including energy penalty improved efficiency and adaptability.

Demonstrated effective simulation-based control for underground mining vehicles.

Abstract

Reinforcement learning control of an underground loader is investigated in simulated environment, using a multi-agent deep neural network approach. At the start of each loading cycle, one agent selects the dig position from a depth camera image of the pile of fragmented rock. A second agent is responsible for continuous control of the vehicle, with the goal of filling the bucket at the selected loading point, while avoiding collisions, getting stuck, or losing ground traction. It relies on motion and force sensors, as well as on camera and lidar. Using a soft actor-critic algorithm the agents learn policies for efficient bucket filling over many subsequent loading cycles, with clear ability to adapt to the changing environment. The best results, on average 75% of the max capacity, are obtained when including a penalty for energy usage in the reward.