Chapter 11: Reinforcement Learning (RL)

“Learning by trial and error—just like humans do!”

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🔹 1. 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 and receiving rewards or punishments.

🧠 Example: A robot learns to walk by trying, failing, adjusting, and trying again—receiving reward when it moves forward.

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🔹 2. Key Components of RL

Component	Meaning
Agent	Learner or decision maker (e.g., robot, AI player)
Environment	The world the agent interacts with (e.g., game, real-world task)
State (S)	Current situation of the agent
Action (A)	Choice made by the agent
Reward (R)	Feedback from the environment
Policy (π)	Strategy that agent follows
Value Function (V)	How good a state is in terms of future reward
Q-Value (Q)	Expected reward of taking action A in state S

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🔹 3. RL vs Supervised Learning

Feature	Reinforcement Learning	Supervised Learning
Data	Agent learns through interaction	Labeled dataset
Feedback	Reward/Punishment	Correct answer (label)
Goal	Maximize reward over time	Minimize prediction error

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🔹 4. Types of RL

🔸 1. Model-Free vs Model-Based

• Model-Free: No knowledge of environment dynamics
  (e.g., Q-Learning, DQN)

• Model-Based: Tries to learn a model of the environment

🔸 2. Exploration vs Exploitation

• Exploration: Try new actions to discover better outcomes

• Exploitation: Use known actions to maximize reward

🔁 Balance is key!

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🔹 5. Important Algorithms in RL

Algorithm	Description
Q-Learning	Learn the Q-values (value of taking action in a state)
SARSA	Similar to Q-learning but updates during action
DQN (Deep Q Network)	Use neural networks for Q-Learning
Policy Gradient	Directly learn the policy
Actor-Critic	Combines value-based & policy-based methods

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🔹 6. Q-Learning Explained

Goal: Learn Q(s, a): What is the best action to take in a state?

Q-Learning Update Formula:

$$Q(s,a) = Q(s,a) + α [r + γ * max(Q(s',a')) - Q(s,a)]$$

Symbol	Meaning
α (alpha)	Learning rate
γ (gamma)	Discount factor
r	Reward
s'	Next state

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🔹 7. Deep Q-Network (DQN)

Combines Q-Learning with Deep Neural Networks.

• Inputs: State (image, game info, etc.)

• Output: Q-values for each action

🏁 Used in:

• Atari Game Solvers

• CartPole Balancing

• Self-driving Simulation

python
model = Sequential([
  Dense(24, input_dim=4, activation='relu'),
  Dense(24, activation='relu'),
  Dense(2, activation='linear')   # Actions: left, right
])

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🔹 8. Popular RL Environments

Use these to train/test RL algorithms:

Platform	Games/Environments
OpenAI Gym	CartPole, MountainCar, LunarLander
Atari	Breakout, Pong, etc.
Unity ML-Agents	3D games
PyBullet / MuJoCo	Physics-based environments

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🔹 9. Applications of RL

Area	Use Case
Robotics	Teaching robots to walk, pick objects
Games	AlphaGo, Dota 2 AI
Finance	Trading agents
Healthcare	Treatment strategy optimization
Self-Driving	Lane control, braking, steering

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🔹 10. Challenges in RL

• Delayed rewards

• Exploration vs Exploitation

• High computation cost

• Training instability

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✅ Chapter Summary

Key Concept	Meaning
Agent	Learns and acts
Environment	World agent interacts with
Reward	Signal of success
Policy	Agent’s strategy
Q-learning	Value-based learning
DQN	Neural network-based Q-learning

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💡 Mini Projects You Can Try:

1. Balance the CartPole using Q-learning (OpenAI Gym)

2. Train an AI to play Pong with Deep Q-Network

3. Simulate a stock trader using RL

4. Create a smart taxi agent using SARSA