Artificial Intelligence
What is Reinforcement Learning
Training an agent through environment interaction and rewards
Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to make decisions through interaction with an environment and receiving rewards or penalties.
Core Components
- Agent — makes decisions and takes actions
- Environment — the world the agent interacts with
- State — current situation in the environment
- Action — agent's choice at each moment
- Reward — feedback from the environment
Key Algorithms
- Q-Learning — learning action-value function
- SARSA — on-policy learning
- Policy Gradient — direct policy optimization
- Actor-Critic — hybrid approach
- Deep Q-Network (DQN) — Q-learning with neural networks
Business Applications
- Pricing optimization
- Recommendation personalization
- Inventory management
- Trading automation
- Ad campaign optimization
Advantages
- Learning without labeled data
- Adapting to environmental changes
- Optimizing long-term outcomes
- Solving complex sequential tasks