Robotic Search & Rescue via Online Multi-task Reinforcement Learning
This addresses the challenge of reducing time and wear-and-tear for robots in multi-task learning, though it is incremental as it builds on existing multi-task RL methods.
The paper tackled the problem of enabling a robot to learn multiple tasks efficiently by using PG-ELLA, an online multi-task reinforcement learning algorithm, and found that it accelerated learning and improved performance compared to Q-learning and policy gradient RL in a search-and-rescue scenario.
Reinforcement learning (RL) is a general and well-known method that a robot can use to learn an optimal control policy to solve a particular task. We would like to build a versatile robot that can learn multiple tasks, but using RL for each of them would be prohibitively expensive in terms of both time and wear-and-tear on the robot. To remedy this problem, we use the Policy Gradient Efficient Lifelong Learning Algorithm (PG-ELLA), an online multi-task RL algorithm that enables the robot to efficiently learn multiple consecutive tasks by sharing knowledge between these tasks to accelerate learning and improve performance. We implemented and evaluated three RL methods--Q-learning, policy gradient RL, and PG-ELLA--on a ground robot whose task is to find a target object in an environment under different surface conditions. In this paper, we discuss our implementations as well as present an empirical analysis of their learning performance.