What is reinforcement learning ?

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment to achieve a goal. It is inspired by behavioral psychology—learning through trial and error with feedback in the form of rewards or penalties.

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Key Concepts in RL

1. Agent
   The learner or decision-maker (e.g., a robot, a software program).

2. Environment
   Everything the agent interacts with (e.g., a game board, a physical world).

3. State (S)
   The current situation of the agent in the environment.

4. Action (A)
   Choices the agent can make.

5. Reward (R)
   Feedback from the environment after an action (positive or negative).

6. Policy (π)
   The strategy the agent uses to decide actions based on states.

7. Goal
   Maximize cumulative reward over time.

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How It Works

• The agent observes the state of the environment.
• It takes an action.
• The environment responds with a new state and a reward.
• The agent updates its policy to improve future decisions.

This process continues until the agent learns an optimal policy.

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Example: Training a Robot to Walk

• Agent: The robot.
• Environment: The floor and surroundings.
• State: Robot's current position and posture.
• Actions: Move left leg, move right leg, adjust balance.
• Reward: +10 for moving forward without falling, -50 for falling.
• Over time, the robot learns which sequence of actions leads to maximum forward movement without falling.

Agent (e.g., robot) interacts with the Environment.

The agent takes an Action → environment responds with a new State and a Reward.

The agent uses this feedback to improve its policy over time.

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Real-World Examples

• Game Playing: AlphaGo learning to play Go better than humans.
• Autonomous Vehicles: Learning to drive safely by trial and error in simulations.
• Recommendation Systems: Learning which content keeps users engaged