Reinforcement Learning
This series provides an overview of reinforcement learning, a type of machine learning that has the potential to solve some control system problems that are too difficult to solve with traditional techniques.
We’ll cover the basics of the reinforcement problem and how it differs from traditional control techniques. We’ll show why neural networks are used to represent unknown functions and how the agent uses rewards from the environment to train them.
By the end of this series, you’ll be better prepared to answer questions like:
- What is reinforcement learning and why should I consider it when solving my control problem?
- How do I set up and solve the reinforcement learning problem?
- What are some of the benefits and drawbacks of reinforcement learning compared to a traditional controls approach?
从工程师的角度了解强化学习概况。强化学习是一种机器学习，可以解决一些非常棘手的控制问题。
本视频通过浏览工作流帮助我们进一步了解强化学习。环境是怎样的？奖励函数如何激励智能体？策略是如何构造的？
Policies and Learning Algorithms
This video provides an introduction to the algorithms that reside within the agent. We’ll cover why we use neural networks to represent functions and why you may have to set up two neural networks in a powerful family of methods called actor-critic.
This video shows how to use the reinforcement learning workflow to get a bipedal robot to walk, and how we can set up the RL problem to look more like a traditional control problem by adding a reference signal to the design.
Overcoming the Practical Challenges of Reinforcement Learning
There are a few challenges that occur when using reinforcement learning for production systems and there are some ways to mitigate them. This video covers the difficulties of verifying the learned solution and what you can do about it.
An Introduction to Multi-Agent Reinforcement Learning
Learn what multi-agent reinforcement learning is and some of the challenges it faces and overcomes.
Why Choose Model-Based Reinforcement Learning?
Compare model-free and model-based reinforcement learning approaches and gain a better understanding of which method to use depending on the situation.
