Reinforcement Learning (RL) is a branch of machine learning where agents learn to make decisions by interacting with an environment. Unlike supervised learning, RL focuses on learning from rewards rather than labeled data. Here's a quick overview of key concepts:
🔹 Core Components
- Agent: The learner or decision-maker.
- Environment: The world the agent interacts with.
- Reward Signal: Feedback from the environment guiding the agent's actions.
- Policy: Strategy that maps states to actions.
- Value Function: Estimates the long-term reward for states/actions.
- Model: Optional, represents the environment's dynamics.
🔹 Key Algorithms
- Q-Learning
- Deep Q-Networks (DQN)
- Policy Gradients
- Actor-Critic Methods
- Monte Carlo Tree Search (MCTS)
🔹 Applications
- Game playing (e.g., AlphaGo)
- Robotics
- Autonomous systems
- Resource management
For deeper exploration, check our Reinforcement Learning Tutorials or join the discussion in the RL Community Forum.