In last article, we have discussed Q-learning and we have seen its desirable convergence attributes. Never the less, Q-learning has one fundamental limitation preventing it from being applicable to more complex RL tasks. During learning, Q-learning keeps the Q-value for every state-action pair. In FrozenLake with 4x4 grid, there are…
In the last article I have explained generalized policy iteration process and described our first reinforcement learning algorithm: Mote Carlo. In this article we will discuss the drawbacks of Monte Carlo and explore two other algorithms that can help the agent overcome shortcomings of Monte Carlo.
Monte Carlo algorithm learns…
In the last article, I have introduced Reinforcement learning Markov Decision Process (MDP) framework, discounted expected rewards and value and policy functions definitions. In this article, we will continue the definition of the MDP framework explaining Bellman and Bellman optimality equations. Additionally we will have describe our first reinforcement learning…
Introduction
Reinforcement learning is an important type of machine learning used in vast range of applications and fields including robotics, genetics, financial applications and recommendation systems to mention a few. In this series of articles, I aim at taking the reader into a journey to learn enough about this topic. The…
