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Reinforcement Learning for Engineers, Part 2: Understanding the 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.

Check out these other resources!
- Reinforcement Learning by Sutton and Barto:
- Reinforcement Learning Course by David Silver:

Part 1: What Is Reinforcement Learning?

Part 3: Policies and Learning Algorithms

Part 4: The Walking Robot Problem

Part 5: Overcoming the Practical Challenges

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