Reinforcement Learning: Essentials
Machine Learning
| Intermediate
- 10 videos | 29m 58s
- Includes Assessment
- Earns a Badge
Explore machine learning reinforcement learning, along with the essential components of reinforcement learning that will assist in the development of critical algorithms for decisionmaking, in this 10-video course. You will examine how to achieve continuous improvement in performance of machines or programs over time, along with key differences between reinforcement learning and machine learning paradigm. Learners will observe how to depict the flow of reinforcement learning by using agent, action, and environment. Next, you will examine different scenarios of state changes and transition processes applied in reinforcement learning. Then examine the reward hypothesis, and learn to recognize the role of rewards in reinforcement learning. You will learn that all goals can be described by maximization of the expected cumulative rewards. Continue by learning the essential steps applied by agents in reinforcement learning to make decisions. You will explore the types of reinforcement learning environments, including deterministic, observable, discrete or continuous, and single-agent or multi-agent. Finally, you will learn how to install OpenAI Gym and OpenAl Universe.
WHAT YOU WILL LEARN
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Define reinforcement learning and describe its essential elements
Recognize the key differences between the reinforcement learning and machine learning paradigms
Depict the flow of reinforcement learning using agent, action, and environment
Describe different state change scenarios and transition processes in reinforcement learning
Recognize the role of rewards in reinforcement learning -
List the essential steps agents take to make decisions in reinforcement learning
Recognize prominent reinforcement learning environment types
Install openai gym and openai universe
List reinforcement learning elements, agents involved in the process and the steps they take, and reinforcement learning environments
IN THIS COURSE
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2m 9s
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3m 38sFind out how to define reinforcement learning and describe its essential elements. FREE ACCESS
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3. Reinforcement Learning and Machine Learning2m 41sUpon completion of this video, you will be able to recognize the key differences between reinforcement learning and machine learning paradigms. FREE ACCESS
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4. Reinforcement Learning Flow3m 14sIn this video, you will learn how to depict the flow of reinforcement learning using an agent, action, and environment. FREE ACCESS
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5. State Change and Transition Process3m 1sAfter completing this video, you will be able to describe different state change scenarios and transition processes in reinforcement learning. FREE ACCESS
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6. Rewards and Reinforcement Learning4m 1sUpon completion of this video, you will be able to recognize the role of rewards in reinforcement learning. FREE ACCESS
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7. Agents in Reinforcement Learning2m 28sUpon completion of this video, you will be able to list the essential steps agents take to make decisions in reinforcement learning. FREE ACCESS
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8. Types of Reinforcement Learning Environment2m 12sAfter completing this video, you will be able to recognize different types of reinforcement learning environments. FREE ACCESS
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9. OpenAI4m 52sIn this video, you will install OpenAI Gym and OpenAI Universe. FREE ACCESS
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10. Exercise: Reinforcement Learning Elements1m 43sAfter completing this video, you will be able to list the elements of reinforcement learning, the agents involved in the process, and the steps they take, as well as reinforcement learning environments. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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