Understanding Reinforcement Learning Environment and Rewards

In this video, we build on our basic understanding of reinforcement learning by exploring the workflow. We cover what an environment is and some of the benefits of training within a simulated environment.
We cover what we ultimately want our agent to do and how crafting a reward function incentivizes the agent to do just that. And lastly, we introduce the need to choose a way to represent a policy-how we want to structure the parameters and logic that make up the decision-making part of the agent.
Watch our full video series about Reinforcement Learning: • Reinforcement Learning
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?
Check out these other resources!
- Reinforcement Learning by Sutton and Barto: bit.ly/2HAYbb4
- Reinforcement Learning Course by David Silver: • RL Course by David Sil...
- Reinforcement Learning Toolbox: bit.ly/2YjuAYa
- Deep Reinforcement Learning for Walking Robots: • Deep Reinforcement Lea...
Check out the individual videos in the series:
• What Is Reinforcement Learning?: • What Is Reinforcement ...
• Understanding the Environment and Rewards: • Understanding Reinforc...
• Policies and Learning Algorithms: • Reinforcement Learning...
• The Walking Robot Problem: • Solving the Walking Ro...
• Overcoming the Practical Challenges: • Overcoming the Practic...
• An Introduction to Multi-Agent Reinforcement Learning: • Introduction to Multi-...
• Why Choose Model-Based Reinforcement Learning?: • Why Choose Model-Based...
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[@BrianBDouglas]

Hey everyone, thanks for watching this video! If you have any questions or comments that you'd like me to see, please leave them under this comment so that I get notified and can respond. Cheers!

[@woodywiest]

Thanks for the super clear and pointed instructions to reinforcement learning for control systems! What advice would you recommend for one beginning in this field? Is there a certain programming course you would recommend to bring one up to speed?

[@BrianBDouglas]

@@woodywiest watching David Silver's lectures (linked in the description) helped me a lot.

[@AmerRanneh]

The long-a
waited series is finally out. Thank you, Brian.

[@soutrikband]

Can you comment on the Stability guarantees of using RL in a control problem ? How can we ensure a control action will stabilize the system ?

[@Barneymeatballs]

These videos are so good. I'm always so impressed how clearly the concepts are communicated. And such exciting concepts they are. Any advice on how to transition into the field of RL, and what the job opportunities are? I'm a current masters student in aerospace engineering, but keeping finding myself drawn further and further into RL

[@user-kq2sn5eq7z]

It is the best tutorial about RL I have watched ever. Thank you a lot.

[@ChristopherLum]

Another great video, I'm looking forward to the rest of the series!

[@miguelramirez4037]

Thanks Mr Brian your videos are excellent

[@therav2007]

really want to see the next video!

[@robertlois773]

nice