In previous sections on Policy Gradient and REINFORCE, we introduced the core concepts of reinforcement learning (RL) algorithms that rely on gradients applied directly to the policy. These are known as policy-based approaches
DQN extends Q-Learning simply by replacing Q-table with a neural network, enabling it to handle high-dimensional and continuous state spaces, such as images in video games [Playing Atari with Deep Reinforcement Learning]. In
Today, we’re going to introduce one of the most well-known value-based reinforcement learning (RL) approaches: Q-Learning.
So, what is Q-Learning?
Q-Learning is a method where an agent learns to make decisions by
Today, we will discuss the REINFORCE algorithm (REward Increment = Nonnegative Factor × Offset Reinforcement × Characteristic Eligibility), which is derived from the Policy Gradient method we previously covered. In short, REINFORCE is a policy-based reinforcement
In Value/Policy-Based Control , we discussed Value-Based and Policy-Based control methods. However, there are still many details to cover before we can progress to more advanced algorithms. One of the key concepts in
