How does the Bellman equation contribute to the Q-learning process in reinforcement learning?
The Bellman equation plays a pivotal role in the Q-learning process within the domain of reinforcement learning, including its quantum-enhanced variants. To understand its contribution, it is essential to consider the foundational principles of reinforcement learning, the mechanics of the Bellman equation, and how these principles are adapted and extended in quantum reinforcement learning using
What is the Bellman equation, and how is it used in the context of Temporal Difference (TD) learning and Q-learning?
The Bellman equation, named after Richard Bellman, is a fundamental concept in the field of reinforcement learning (RL) and dynamic programming. It provides a recursive decomposition for solving the problem of finding an optimal policy. The Bellman equation is central to various RL algorithms, including Temporal Difference (TD) learning and Q-learning, which are pivotal in
How does the Bellman equation facilitate the process of policy evaluation in dynamic programming, and what role does the discount factor play in this context?
The Bellman equation is a cornerstone in the field of dynamic programming and plays a pivotal role in the evaluation of policies within the framework of Markov Decision Processes (MDPs). In the context of reinforcement learning, the Bellman equation provides a recursive decomposition that simplifies the process of determining the value of a policy. This
How does the Q-learning algorithm work?
Q-learning is a type of reinforcement learning algorithm that was first introduced by Watkins in 1989. It is designed to find the optimal action-selection policy for any given finite Markov decision process (MDP). The goal of Q-learning is to learn the quality of actions, which is represented by the Q-values. These Q-values are used to
- Published in Artificial Intelligence, EITC/AI/ARL Advanced Reinforcement Learning, Introduction, Introduction to reinforcement learning