Reinforcement Learning
Learning through interaction and reward based decision making.
Overview
Reinforcement Learning trains agents to make sequences of decisions by learning from interactions with an environment. The agent learns optimal strategies through trial and error, receiving rewards for good actions.
Key Features
Reward-based learning
Policy optimization
Value estimation
Exploration strategies
Multi-agent learning
Continuous learning
Use Cases
Game playing
Robotics control
Resource management
Trading strategies
Traffic optimization
Personalization
Benefits
- Optimize complex decisions
- Learn from experience
- Handle dynamic environments
- Discover optimal strategies