Reinforcement Learning (RL) is a type of Machine Learning where an agent learns by interacting with an environment and receiving rewards or penalties based on its actions.
The goal is to learn the best strategy (policy) to maximize total rewards over time.
The agent takes an action
The environment responds
The agent receives a reward or penalty
The agent learns from this feedback
The process repeats to improve performance
Agent: Learner or decision maker
Environment: Where the agent operates
Action: What the agent does
State: Current situation of the agent
Reward: Feedback received after an action
State → Action → Reward → New State → Learning
Game playing (Chess, Ludo, Video Games)
Self-driving cars
Robot navigation
Recommendation systems
Q-Learning
SARSA
Deep Q Network (DQN)
Learns optimal behavior through experience
No labeled data required
Requires a lot of time and computation
Training can be complex
Reinforcement Learning focuses on learning by trial and error, making it powerful for decision-making tasks where actions affect future outcomes.
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