Welcome to our Reinforcement Learning (RL) course! This is a comprehensive guide that will take you through the fundamentals of RL and help you understand how to build intelligent agents that learn from their environment.
What is Reinforcement Learning?
Reinforcement Learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to achieve a goal. The agent learns from the consequences of its actions, which are represented as rewards or penalties.
Course Outline
- Introduction to RL
- Definition and history
- Key concepts and terms
- Markov Decision Processes (MDPs)
- Definition and properties
- Value and policy functions
- Q-Learning and SARSA
- Temporal difference learning
- Exploration vs. exploitation
- Deep Reinforcement Learning
- Introduction to neural networks
- Deep Q-Networks (DQN)
- Policy Gradient methods
- Practical Applications
- Game playing
- Robotics
- Autonomous vehicles
Key Concepts
- State: The current situation or context of the agent.
- Action: A decision made by the agent.
- Reward: A signal received by the agent indicating the success or failure of its action.
- Policy: A set of rules that the agent uses to choose actions.
Resources
For further reading, check out our Introduction to Machine Learning course.