# Pythonで学ぶGymnasiumによるReinforcement Learning
This is a DataCamp course: 強化学習の旅を始めましょう！エージェントが相互作用を通じて環境を解決する方法を学びます。
## Course Details
- **Duration:** ~4h
- **Level:** Advanced
- **Instructor:** Fouad Trad
- **Students:** ~19,440,000 learners
- **Subjects:** Python, Artificial Intelligence
- **Content brand:** DataCamp
- **Practice:** Hands-on practice included
- **Prerequisites:** Supervised Learning with scikit-learn, Python Toolbox, Introduction to NumPy
## Learning Outcomes
- Python
- Artificial Intelligence
- Pythonで学ぶGymnasiumによるReinforcement Learning
## Traditional Course Outline
1. Introduction to Reinforcement Learning - Dive into the exciting world of Reinforcement Learning (RL) by exploring its foundational concepts, roles, and applications. Navigate through the RL framework, uncovering the agent-environment interaction. You'll also learn how to use the Gymnasium library to create environments, visualize states, and perform actions, thus gaining a practical foundation in RL concepts and applications.
2. Model-Based Learning - Delve deeper into the world of RL focusing on model-based learning. Unravel the complexities of Markov Decision Processes (MDPs), understanding their essential components. Enhance your skill set by learning about policies and value functions. Gain expertise in policy optimization with policy iteration and value Iteration techniques.
3. Model-Free Learning - Embark on a journey through the dynamic realm of Model-Free Learning in RL. Get introduced to to the foundational Monte Carlo methods, and apply first-visit and every-visit Monte Carlo prediction algorithms. Transition into the world of Temporal Difference Learning, exploring the SARSA algorithm. Finally, dive into the depths of Q-Learning, and analyze its convergence in challenging environments.
4. Advanced Strategies in Model-Free RL - Dive into advanced strategies in Model-Free RL, focusing on enhancing decision-making algorithms. Learn about Expected SARSA for more accurate policy updates and Double Q-learning to mitigate overestimation bias. Explore the Exploration-Exploitation Tradeoff, mastering epsilon-greedy and epsilon-decay strategies for optimal action selection. Tackle the Multi-Armed Bandit Problem, applying strategies to solve decision-making challenges under uncertainty.
## Resources and Related Learning
**Related tracks:** 機械学習の基礎 Pythonで, 強化学習 Pythonで
## Attribution & Usage Guidelines
- **Canonical URL:** https://www.datacamp.com/courses/reinforcement-learning-with-gymnasium-in-python
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Pythonで学ぶGymnasiumによるReinforcement Learning
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更新日 2024/09PythonArtificial Intelligence4時間15 ビデオ52 演習4,400 XP12,112達成証明書
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前提条件
Supervised Learning with scikit-learnPython ToolboxIntroduction to NumPy1
Introduction to Reinforcement Learning
Dive into the exciting world of Reinforcement Learning (RL) by exploring its foundational concepts, roles, and applications. Navigate through the RL framework, uncovering the agent-environment interaction. You'll also learn how to use the Gymnasium library to create environments, visualize states, and perform actions, thus gaining a practical foundation in RL concepts and applications.
2
Model-Based Learning
Delve deeper into the world of RL focusing on model-based learning. Unravel the complexities of Markov Decision Processes (MDPs), understanding their essential components. Enhance your skill set by learning about policies and value functions. Gain expertise in policy optimization with policy iteration and value Iteration techniques.
3
Model-Free Learning
Embark on a journey through the dynamic realm of Model-Free Learning in RL. Get introduced to to the foundational Monte Carlo methods, and apply first-visit and every-visit Monte Carlo prediction algorithms. Transition into the world of Temporal Difference Learning, exploring the SARSA algorithm. Finally, dive into the depths of Q-Learning, and analyze its convergence in challenging environments.
4
Advanced Strategies in Model-Free RL
Dive into advanced strategies in Model-Free RL, focusing on enhancing decision-making algorithms. Learn about Expected SARSA for more accurate policy updates and Double Q-learning to mitigate overestimation bias. Explore the Exploration-Exploitation Tradeoff, mastering epsilon-greedy and epsilon-decay strategies for optimal action selection. Tackle the Multi-Armed Bandit Problem, applying strategies to solve decision-making challenges under uncertainty.
Pythonで学ぶGymnasiumによるReinforcement Learning
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