Advanced Reinforcement Learning: Implementation
Machine Learning
| Intermediate
- 11 Videos | 1h 34m 16s
- Includes Assessment
- Earns a Badge
In this 11-video course, learners can examine the role of reward and discount factors in reinforcement learning, as well as the multi-armed bandit problem and approaches to solving it for machine learning. You will begin by learning how to install the Markov Decision Policy (MDP) toolbox and implement the Discounted Markov Decision Process using the policy iteration algorithm. Next, examine the role of reward and discount factors in reinforcement learning, and the multi-armed bandit problem and solutions. Learn about dynamic programming, policy evaluation, policy iteration, value iteration, and characteristics of Bellman equation. Then learners will explore reinforcement learning agent components and applications; work with reinforcement learning agents using Keras and OpenAI Gym; describe reinforcement learning algorithms and the reinforcement learning taxonomy defined by OpenAI; and implement deep Q-learning with Keras. Finally, observe how to train deep neural networks (DNN) with reinforcement learning for time series forecasting. In the closing exercise, you will recall approaches for resolving the multi-armed bandit problem, list reinforcement learning agent components, and implement deep Q-learning by using Keras and OpenAI Gym.
WHAT YOU WILL LEARN
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discover the key concepts covered in this courseinstall the Markov Decision Policy toolbox and implement the Discounted Markov Decision Process using the policy iteration algorithmrecognize the role of reward and discount factors in reinforcement learningdescribe the multi-armed bandit problem and different approaches of solving this problemdescribe dynamic programming, policy evaluation, policy iteration, value iteration, and characteristics of Bellman equationlist reinforcement learning agent components and reinforcement agent applications
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work with reinforcement learning agents using Keras and OpenAI Gymdescribe reinforcement learning algorithms and the reinforcement learning taxonomy defined by OpenAIimplement deep reinforcement learning or deep Q-learning using Keras and OpenAI Gymrecognize how to train deep neural networks using reinforcement learning for time series forecastingrecall approaches for resolving the multi-armed bandit problem, list reinforcement learning agent components, and implement deep Q-learning using Keras and OpenAI Gym
IN THIS COURSE
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1.Course Overview1m 42sUP NEXT
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2.Installing the Markov Decision Process Toolbox8m 8s
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3.Rewards and Discounts11m 15s
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4.Multi-Armed Bandit Problem10m 35s
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5.Dynamic Programming and Bellman Equation6m 2s
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6.Reinforcement Learning Agent and Its Components7m 13s
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7.Reinforcement Learning with OpenAI Gym and Keras17m 3s
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8.Reinforcement Learning Taxonomy by OpenAI8m 6s
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9.Deep Q-Learning Implementation9m 48s
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10.Training DNN Using Reinforcement Learning6m 48s
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11.Exercise: Implementing Deep Q-Learning7m 38s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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