Reinforcement Learning (RL) is a critical subfield of Deep Learning that focuses on training agents to make decisions by interacting with an environment. Unlike supervised learning, RL relies on reward signals to guide the agent toward optimal actions.
Key Concepts 📚
- Agent: The learner or decision-maker (e.g., a robot, game AI).
- Environment: The world the agent interacts with (e.g., a simulation, real-world scenario).
- Reward: Feedback given to the agent for its actions (e.g., +1 for success, -1 for failure).
- Policy: Strategy the agent uses to choose actions (e.g., greedy, epsilon-greedy).
- Q-Learning: A popular algorithm for learning optimal policies through value function approximation.
Applications 🌍
- Game playing (e.g., AlphaGo, Dota 2 AI)
- Robotics (e.g., autonomous navigation)
- Autonomous vehicles (e.g., self-driving car control)
- Resource management (e.g., energy optimization)
Deep Reinforcement Learning 🧠
Combining RL with Deep Neural Networks allows agents to handle complex, high-dimensional data:
- Deep Q-Networks (DQN): Use neural networks to approximate Q-values.
- Policy Gradients: Directly optimize policies using gradient ascent.
- Actor-Critic Methods: Balance exploration and exploitation with two networks.
For deeper insights, explore our guide on Deep Learning fundamentals.