mirror of
https://github.com/huggingface/deep-rl-class.git
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135 lines
4.2 KiB
YAML
135 lines
4.2 KiB
YAML
- title: Unit 0. Welcome to the course
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sections:
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- local: unit0/introduction
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title: Welcome to the course 🤗
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- local: unit0/setup
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title: Setup
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- local: unit0/discord101
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title: Discord 101
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- title: Unit 1. Introduction to Deep Reinforcement Learning
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sections:
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- local: unit1/introduction
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title: Introduction
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- local: unit1/what-is-rl
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title: What is Reinforcement Learning?
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- local: unit1/rl-framework
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title: The Reinforcement Learning Framework
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- local: unit1/tasks
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title: The type of tasks
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- local: unit1/exp-exp-tradeoff
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title: The Exploration/ Exploitation tradeoff
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- local: unit1/two-methods
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title: The two main approaches for solving RL problems
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- local: unit1/deep-rl
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title: The “Deep” in Deep Reinforcement Learning
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- local: unit1/summary
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title: Summary
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- local: unit1/glossary
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title: Glossary
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- local: unit1/hands-on
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title: Hands-on
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- local: unit1/quiz
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title: Quiz
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- local: unit1/conclusion
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title: Conclusion
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- local: unit1/additional-readings
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title: Additional Readings
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- title: Bonus Unit 1. Introduction to Deep Reinforcement Learning with Huggy
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sections:
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- local: unitbonus1/introduction
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title: Introduction
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- local: unitbonus1/how-huggy-works
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title: How Huggy works?
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- local: unitbonus1/train
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title: Train Huggy
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- local: unitbonus1/play
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title: Play with Huggy
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- local: unitbonus1/conclusion
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title: Conclusion
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- title: Live 1. How the course work, Q&A, and playing with Huggy
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sections:
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- local: live1/live1
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title: Live 1. How the course work, Q&A, and playing with Huggy 🐶
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- title: Unit 2. Introduction to Q-Learning
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sections:
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- local: unit2/introduction
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title: Introduction
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- local: unit2/what-is-rl
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title: What is RL? A short recap
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- local: unit2/two-types-value-based-methods
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title: The two types of value-based methods
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- local: unit2/bellman-equation
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title: The Bellman Equation, simplify our value estimation
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- local: unit2/mc-vs-td
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title: Monte Carlo vs Temporal Difference Learning
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- local: unit2/mid-way-recap
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title: Mid-way Recap
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- local: unit2/mid-way-quiz
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title: Mid-way Quiz
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- local: unit2/q-learning
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title: Introducing Q-Learning
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- local: unit2/q-learning-example
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title: A Q-Learning example
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- local: unit2/q-learning-recap
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title: Q-Learning Recap
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- local: unit2/glossary
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title: Glossary
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- local: unit2/hands-on
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title: Hands-on
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- local: unit2/quiz2
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title: Q-Learning Quiz
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- local: unit2/conclusion
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title: Conclusion
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- local: unit2/additional-readings
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title: Additional Readings
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- title: Unit 3. Deep Q-Learning with Atari Games
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sections:
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- local: unit3/introduction
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title: Introduction
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- local: unit3/from-q-to-dqn
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title: From Q-Learning to Deep Q-Learning
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- local: unit3/deep-q-network
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title: The Deep Q-Network (DQN)
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- local: unit3/deep-q-algorithm
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title: The Deep Q Algorithm
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- local: unit3/glossary
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title: Glossary
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- local: unit3/hands-on
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title: Hands-on
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- local: unit3/quiz
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title: Quiz
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- local: unit3/conclusion
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title: Conclusion
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- local: unit3/additional-readings
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title: Additional Readings
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- title: Bonus Unit 2. Automatic Hyperparameter Tuning with Optuna
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sections:
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- local: unitbonus2/introduction
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title: Introduction
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- local: unitbonus2/optuna
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title: Optuna
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- local: unitbonus2/hands-on
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title: Hands-on
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- title: Unit 4. Policy Gradient with PyTorch
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sections:
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- local: unit4/introduction
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title: Introduction
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- local: unit4/what-are-policy-based-methods
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title: What are the policy-based methods?
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- local: unit4/advantages-disadvantages
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title: The advantages and disadvantages of Policy-based methods
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- local: unit4/policy-gradient
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title: Diving deeper into Policy-gradient methods
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- local: unit4/hands-on
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title: Hands-on
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- local: unit4/quiz
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title: Quiz
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- local: unit4/conclusion
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title: Conclusion
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- local: unit4/additional-readings
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title: Additional Readings
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- title: What's next? New Units Publishing Schedule
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sections:
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- local: communication/publishing-schedule
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title: Publishing Schedule
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