Reinforcement Learning Basics with OpenAI Gym (available)
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Welcome to Chapter 1! (3:39)
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OpenAI Gym Installation (12:43)
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Jupyter Installation (9:32)
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Setting Up a RL Problem (12:54)
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Exercise: Set Up the MountainCar-v0 Environment
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The Agent and Its Environment (15:37)
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Exercise: Investigate Observations in the MountainCar-v0 Environment
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Actions (18:21)
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Exercise: Actions in the MountainCar Environment
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Rewards (19:09)
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Exercise: Investigate Rewards in the MountainCar Environment
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Goals and Corresponding Reward Functions (16:03)
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Episodes (23:55)
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Exercise: What are the Terminal States in MountainCar?
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Exercise: Calculate Average Total Reward Per Episode
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See You in Chapter 2! (3:29)
How to Maximize Rewards (available)
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Markov Decision Process (22:45)
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RL vs. Other Forms of ML (Supervised/Unsupervised Learning) (11:38)
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Policy (20:34)
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Exercise: Implement the Sampling Function for the Epsilon Pole Direction Policy
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Model Based vs. Model Free Learning (6:57)
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Modifying Gym Environments With Wrappers (56:40)
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Exercise: Modify the CartPole-v0 Environment to Return Rounded Observations
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Value Function (20:50)
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Calculating Value Function Samples (25:56)
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Exercise: Calculate Value Samples for Pole Direction Policy
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Discounted Reward Sum (34:33)
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Exercise: Calculate Value Samples Using Discounted Reward Sum
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Action Value Function (20:35)
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Calculating Q Values (28:06)
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Exercise: Calculate Average Values of States Over Many Episodes
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Bellman Expectation Equation for the Value Function (21:09)
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Exercise: Verify the Bellman Expectation Equation for the Q-Value Function
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Comparing Policies (14:22)
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Policy Improvement (25:22)
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Greedy Policy Improvement in CartPole-v0 (22:51)
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Exercise: Implement Greedy Policy Sampling Function with Random Tie Breaking
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Exercise: Implementing Sampling Functions for any Environment with Discrete Actions
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Optimal Policy (4:46)
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Exploration vs. Exploitation (20:27)
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Exercise: Plot growth of state-action pairs in exploration mode
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Epsilon Greedy Policy (15:40)
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Iterative Epsilon Greedy Policy Improvement (19:06)
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Exercise: Implement an Exponential Schedule for Epsilon
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Important Announcement
