Nowadays, Deep Reinforcement Learning (RL) is one of the hottest topics in the Data Science community. The fast development of RL has resulted in the growing demand for easy to understand and convenient to use RL tools. That’s why it is important to pick a library that will be quick, reliable, and relevant for your RL task.
In this article, I’ll show you how to install MuJoCo on your Mac/Linux machine in order to run continuous control environments from OpenAI’s Gym. These environments include classic ones like HalfCheetah, Hopper, Walker, Ant, and Humanoid and harder ones like object manipulation with a robotic arm or robotic hand dexterity. I’ll also discuss additional agent diagnostics provided by the environments that you might not have considered before.
In this post, I’ll share with you my library of environments that support training reinforcement learning (RL) agents. The basis for RL research, or even playing with or learning RL, is the environment. It’s where you run your algorithm to evaluate how good it is. We’re going to explore 23 different benchmarks, so I guarantee you’ll find something interesting!
In part one, we discussed the importance of focusing on a well-defined project. In this article, we’re diving even deeper, because we’re going to talk about the fractal nature of creative work, or why it’s hard to do meaningful work when your projects are built from sub-projects, which are built from sub-sub-projects, that are built from… you know the drill.
This blog post is the start of a series on managing research work. In it, you’ll see how I maintain a helpful list of tasks, and manage my teams to finish these tasks.
Logging is often a significant issue, as frameworks have different approaches to it. In this article I’ll show you how logging is implemented in popular reinforcement learning frameworks.
Based on simply watching how an agent acts in the environment it is hard to tell anything about why it behaves this way and how it works internally. That’s why it is crucial to establish metrics that tell WHY the agent performs in a certain way.