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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1.Course Overview2m 3sUP NEXT
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2.Reinforcement Learning Types6m 45s
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3.Reinforcement Learning with Keras and Python3m 12s
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4.Markov Decision Process2m 54s
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5.Q-Learning Concepts3m 33s
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6.TensorFlow Installation3m 15s
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7.Reinforcement Learning and TensorFlow3m 47s
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8.Q-learning and Python4m 58s
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9.Exercise: Reinforcement Learning with Python4m 1s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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