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- AI
Reinforcement Learning
0 Hours
Skill IQ
Reinforcement Learning is a branch of machine learning where agents learn to make decisions by interacting with an environment to achieve a goal. It especially well-suited for solving complex, sequential problems like robotics, game playing, and autonomous systems.
This path introduces you to Reinforcement Learning, from understanding agents, environments, and rewards to exploring key algorithms like Q-learning, Monte Carlo methods, and modified Reinforcement Learning. You’ll progress from basic concepts to implementing Advantage Actor-Critic, Soft Actor-Critic, and Proximal Policy Optimization.
Content in this path
Reinforcement Learning
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What You'll Learn
- How to understand the basics of Reinforcement Learning
- How to get started with Gymnasium
- How to implement Q-learning
- How to apply Monte Carlo Methods
- How to use Actor-Critic Methods and Advantage Estimation
- How to explore and modify Reinforcement Learning techniques
Prerequisites
- While there are no specific courses or paths necessary to view before completing this course, it's helpful if learners have a basic understanding of machine learning.
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