Reinforcement Learning (RL): how robots learn from their environment
Reinforcement Learning (RL) has been increasingly applied in recent years in the world of autonomous robotics, especially in the development of what have been called ‘curious robots‘, i.e. robots programmed to mimic human curiosity about the external environment.
Indeed, in general, one of the fundamental problems of autonomous robots concerns their ability to autonomously generate strategies to solve a problem, or to autonomously explore an environment. RL makes it possible to improve the robot’s performance in both these areas. Reinforcement learning is one of the three basic paradigms of machine learning, together with supervised learning and unsupervised learning. In the field of ‘open ended robotics’, RL is used to allow the robot to explore and learn from an environment even in the absence of an explicit goal. Briefly, how RL works in this context is as follows: the robot starts to explore a part of the environment with sensors and actuators, i.e. mechanical arms. As soon as the environment is known beyond a certain threshold, the RL algorithm decreases the reward, i.e. positive ‘reinforcement’ – hence Reinforcement learning – in exploring that part of the environment, and forces the robot to explore a new portion. In this way, the robot is driven, autonomously, by a curiosity-like principle. One of the major advantages of using reinforcement learning in the development of ‘curious robots’ is that it allows these robots to learn from their environment in a more natural way. Traditional programming techniques require engineers to specify every step a robot must perform to complete a task, which can be time-consuming and inefficient, especially if the robot finds applications in unpredictable and changing environments. Reinforcement learning, on the other hand, allows robots to learn autonomously from their environment and develop the best interaction strategies. These techniques can also be used to make the robot discover, in a trial-and-error procedure, which is the shortest way out of a maze. In general, RL works very well for exploratory objectives, and for interaction with extremely unpredictable environments, where normal programming techniques would certainly fail. The evolution of this approach could lead in the coming years to robots capable of exploring vast portions of the environment, for long periods of time, without the need for any human supervision. Such technology has applications in multiple fields, both civil and military.
Despite these advantages, there are also some potential risks associated with the use of reinforcement learning in curious robots. One of the main concerns is that reinforcement learning algorithms can be difficult to interpret, which makes it complex to understand how a robot makes decisions and to predict how it will behave in a given situation. Furthermore, reinforcement learning algorithms carry the risk that a robot will learn to perform sub-optimal or even harmful actions if the interpretation of environmental feedback is ineffective.
Overall, although there are certainly risks associated with the use of reinforcement learning in robotics, the advantages of this technique can be significant. By enabling robots to learn complex tasks and adapt more easily to new environments, reinforcement learning can help make robots more versatile and efficient. As long as these algorithms are used carefully and with proper supervision, they can be a powerful tool for improving performance and advancing the field of robotics.