Reinforcement Learning: Tools & Frameworks
Machine Learning
| Intermediate
- 9 videos | 34m 26s
- Includes Assessment
- Earns a Badge
This 9-video course explores how to implement machine learning reinforcement learning by examining the terminology, including agents, the environment, state, and policy. This course demonstrates how to implement reinforcement learning by using Keras and Python; how to ensure that you can build a model; and how to launch and use Ubuntu, and VI editor to do score calculations. First, learn the role of the Markov decision process in which the agent observes the environment, with output consisting of a reward and the next state, and then acts upon it. You will explore Q-learning, a model-free reinforcement learning technique, an asynchronous dynamic programming approach, and will learn about the Q-learning rule, and Deep Q-learning. Next, learn the steps to install TensorFlow for reinforcement learning, as well as framework, which is used for reinforcement learning provided by OpenAI. Then learn how to implement TensorFlow for reinforcement learning. Finally, you will learn to implement Q-learning using Python, and then utilize capabilities of OpenAl Gym and FrozenLake.
WHAT YOU WILL LEARN
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recognize the different types of reinforcement learning that can be implemented for decision-making
implement reinforcement learning using Keras and Python
identify the role of the Markov decision process in reinforcement learning
describe Q-learning, Q-learning rule, and deep Q-learning -
install TensorFlow
implement reinforcement learning using TensorFlow
implement Q-learning using Python
implement reinforcement learning using Python and TensorFlow and implement Q-learning using Python
IN THIS COURSE
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2m 3s
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6m 45sAfter completing this video, you will be able to recognize the different types of reinforcement learning that can be implemented for decision-making. FREE ACCESS
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3. Reinforcement Learning with Keras and Python3m 12sFind out how to implement reinforcement learning using Keras and Python. FREE ACCESS
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4. Markov Decision Process2m 54sFind out how to identify the role of the Markov decision process in reinforcement learning. FREE ACCESS
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5. Q-Learning Concepts3m 33sAfter completing this video, you will be able to describe Q-learning, the Q-learning rule, and deep Q-learning. FREE ACCESS
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6. TensorFlow Installation3m 15sIn this video, you will learn how to install TensorFlow. FREE ACCESS
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7. Reinforcement Learning and TensorFlow3m 47sIn this video, you will learn how to implement reinforcement learning using TensorFlow. FREE ACCESS
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8. Q-learning and Python4m 58sDuring this video, you will learn how to implement Q-learning in Python. FREE ACCESS
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9. Exercise: Reinforcement Learning with Python4m 1sIn this video, you will learn how to implement reinforcement learning using Python and TensorFlow, and how to implement Q-learning using Python. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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