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Comprehending intelligence and developing intelligent machines represent significant scientific endeavors of our era. The capacity to learn from experience is a fundamental aspect of intelligence for both machines and living organisms. This principle underpins the field of reinforcement learning, a crucial area within artificial intelligence and machine learning.
In a remarkably visionary report from 1948, Alan Turing, considered the father of modern computer science, proposed constructing machines capable of exhibiting intelligent behavior. He also addressed the “training” of such machines “by means of rewards and punishments.”
Turing’s concepts ultimately paved the way for the advancement of reinforcement learning, a specialized domain of artificial intelligence. Reinforcement learning focuses on creating intelligent agents by instructing them to optimize rewards through interaction within their environment.
As a machine learning researcher, it is particularly noteworthy that reinforcement learning pioneers Andrew Barto and Richard Sutton have been honored with the prestigious 2024 ACM Turing Award.
Understanding Reinforcement Learning
Animal trainers recognize that behavior can be shaped by rewarding favorable actions. For instance, a dog trainer might offer a treat when a dog performs a trick correctly. This action strengthens the desired behavior, increasing the likelihood of the dog repeating the trick accurately in the future. Reinforcement learning draws upon this principle from animal psychology.
However, reinforcement learning centers on training computational agents, not animals. An agent can be a software program, such as a chess-playing application. Conversely, an agent may also be a physical entity, like a robot learning to perform household tasks. Similarly, an agent’s environment can be virtual, such as a chessboard or a simulated world within a video game, or it can be a real-world setting, like a house where a robot is operating.
Similar to animals, an agent can perceive elements of its surroundings and execute actions. A chess-playing agent can assess the chessboard layout and make strategic moves. A robot can utilize cameras and microphones to perceive its environment and employ motors to navigate the physical world.
Agents are also programmed with objectives set by their human designers. A chess-playing agent’s objective is to win the game. A robot’s goal might be to assist a homeowner with domestic chores.
The core challenge of reinforcement learning in AI lies in designing agents that successfully achieve their goals by perceiving and acting within their environments. Reinforcement learning posits a significant hypothesis: all goals can be attained by designing a measurable signal, termed reward, and enabling the agent to optimize the cumulative sum of rewards received.
Researchers acknowledge that the universal validity of this assertion is not yet fully established due to the vast spectrum of potential goals. Consequently, it is frequently referred to as the reward hypothesis.
In certain instances, selecting a reward signal that aligns with a specific goal is straightforward. For a chess-playing agent, the reward structure could be +1 for a victory, 0 for a draw, and -1 for a defeat. However, devising a suitable reward signal for a helpful robotic household assistant presents greater complexity. Nevertheless, the range of applications where reinforcement learning researchers have successfully engineered effective reward signals is continually expanding.
A notable triumph of reinforcement learning occurred in mastering the board game Go. Experts initially considered Go significantly more challenging for machines than chess. The company DeepMind, now Google DeepMind, employed reinforcement learning to develop AlphaGo. In 2016, AlphaGo defeated top-ranked Go player Lee Sedol in a five-game match.
More recently, reinforcement learning has been applied to enhance chatbots, including ChatGPT, making them more beneficial and improving their reasoning capabilities.
Origins of Reinforcement Learning
However, none of these successes were anticipated in the 1980s. It was during this period that Barto and Sutton, then a doctoral student, introduced reinforcement learning as a comprehensive problem-solving framework. Their inspiration stemmed from animal psychology, control theory – the application of feedback to influence system behavior – and optimization, a mathematical field focused on identifying the optimal choice among available options. They equipped the research community with robust mathematical foundations that have proven enduring. They also developed algorithms that have become fundamental tools within the field.
It is uncommon for pioneers in a field to dedicate time to writing a comprehensive textbook. Renowned examples like “The Nature of the Chemical Bond” by Linus Pauling and “The Art of Computer Programming” by Donald E. Knuth are memorable precisely because they are rare occurrences. Sutton and Barto’s “Reinforcement Learning: An Introduction” was initially published in 1998, with a second edition released in 2018. Their book has profoundly impacted a generation of researchers and has been cited over 75,000 times.
Reinforcement learning has also exerted an unexpected influence on neuroscience. The neurotransmitter dopamine plays a critical role in reward-driven behaviors in both humans and animals. Researchers have utilized specific algorithms developed in reinforcement learning to interpret experimental observations within the dopamine system of humans and animals.
Barto and Sutton’s foundational contributions, foresight, and advocacy have been instrumental in the growth of reinforcement learning. Their work has inspired extensive research, impacted real-world applications, and attracted substantial investments from technology companies. Reinforcement learning researchers will undoubtedly continue to advance the field, building upon their significant achievements.