Understanding Reinforcement Learning Hands-On: Markov Decision Processes

Understanding Reinforcement Learning Hands-On: Markov Decision Processes

Today, we’re going to start exploring new environments, using a very powerful tool, the Markov Decision Processes (MDPs). We will develop a strong foundation for describing environments, as well as establish some terminology that will allow us to go further into RL.

  1. Introduction
  2. Multi-Armed Bandits | Notebook
  3. Non-StationaryNotebook
  4. Markov Decision Processes | Notebook

Welcome back to our series on Reinforcement Learning. This is the fourth entry, so you’re encouraged to look back to the previous articles as we’re building up from there. Previously, we talked about a pretty basic situation called the Multi-Armed Bandit Scenario, which allowed us to start thinking about how to learn through interaction.

Today, we’re going to start exploring new environments, using a very powerful tool, the Markov Decision Processes (MDPs). We will develop a strong foundation for describing environments, as well as establish some terminology that will allow us to go further into RL.

The pitfall of Multi-Armed Bandits:

We’ve already talked a lot about the Multi-Armed Bandit scenario, and developed strong ideas and strategies to deal with them. Yet, the world around us doesn’t look as simple as what we have presented so far. It’s finally time to talk about why this environment is considered ‘basic’, and how we can describe more complex scenarios.

The Multi-Armed Bandit scenario presented us with a single situation: standing in front of many armed bandits, where each arm was an independent action we could take. This scenario doesn’t change after an interaction; we’re still presented with the same situation and the same set of actions. The most complex variation presented was the non-stationary scenario, but still our decisions only looked at the same situation, and our actions didn’t influence anything in the future.

In the real world, we’re always presented with new situations, and our actions affect where we will find ourselves in the future. If we want to develop agents that are capable of dealing with the real world, we need to make a more robust definition for the environments. Here’s where the Markov Decision Processes come to the rescue!

machine-learning artificial-intelligence reinforcement-learning game-theory

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