Fundamentals
Reinforcement Learning
Learning through interaction with environment and rewards
What is Reinforcement Learning?
Reinforcement Learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative reward. The agent learns from the consequences of its actions rather than from being explicitly taught.
Key Points
1
Agent-environment interaction
2
Learning from rewards and penalties
3
Explores vs. exploits trade-off
4
Applications in games and robotics
Practical Examples
Game-playing AI (AlphaGo)
Autonomous driving
Robot control
Resource management