Reinforcement Learning (RL) is a branch of Artificial Intelligence where agents learn to make decisions by interacting with an environment. Unlike supervised learning, RL focuses on learning through rewards and penalties.
📌 Key Concepts
Agent-Environment Interaction
- The agent takes actions in an environment to maximize cumulative rewards.
- Example: A robot navigating a maze to reach a goal.
Reward Function
- Defines what the agent should optimize.
- Can be positive (✅) or negative (❌) based on task goals.
Q-Learning
- A model-free algorithm that learns the value of actions in specific states.
- Formula: $ Q(s,a) \leftarrow Q(s,a) + \alpha [r + \gamma \max_{a'} Q(s',a') - Q(s,a)] $
Deep Q Networks (DQN)
- Combines Q-learning with deep neural networks for complex environments.
- Uses experience replay and target networks for stability.
Policy Gradient Methods
- Directly optimize the policy (the strategy the agent uses) using gradient ascent.
- Suitable for high-dimensional action spaces.
🎮 Applications of RL
- Game Playing: Mastering games like Chess, Go, or Atari classics.
- Robotics: Controlling autonomous robots for navigation or manipulation.
- Autonomous Driving: Decision-making for path planning and obstacle avoidance.
- Recommendation Systems: Personalizing user experiences through adaptive policies.
📘 Further Reading
- RL Basics Introduction for foundational concepts.
- Advanced Topics in RL to dive deeper into algorithms.
Let me know if you'd like to explore specific RL frameworks like TensorFlow or PyTorch! 🚀