C. Watkins' 1989 Ph.D. thesis, “Learning from Delayed Rewards,” completed at the University of Cambridge, introduces a fundamental advancement in the field of reinforcement learning through the development of the Q-learning algorithm. This work addresses the challenge of learning optimal actions in environments where rewards are not immediately received, a common scenario in real-world decision-making problems.
In his thesis, Watkins presents the Q-learning algorithm, a model-free reinforcement learning method that enables an agent to learn the value of actions in various states of the environment. This value, known as the Q-value, represents the expected cumulative reward of taking a specific action in a given state and following the optimal policy thereafter. The key innovation of Q-learning is its ability to learn these values iteratively through trial and error, even when the rewards are delayed.
Watkins' algorithm updates Q-values using a simple yet powerful update rule, which incorporates both the immediate reward and the maximum estimated future rewards. This approach allows the agent to progressively improve its policy by continually refining its understanding of the long-term consequences of its actions. Importantly, Q-learning is proven to converge to the optimal policy, given sufficient exploration of the state-action space and appropriate learning parameters.
The introduction of Q-learning had a profound impact on the field of reinforcement learning, providing a robust and versatile framework for solving complex decision-making problems. Watkins' work laid the groundwork for many subsequent advancements in reinforcement learning and has been instrumental in applications ranging from robotics to game playing and beyond. His thesis remains a cornerstone in the literature, highlighting the significance of learning from delayed rewards in the pursuit of intelligent, autonomous systems.