Explain the basic concepts of Reinforcement Learning. Also explain the exploration-exploitation trade-off and the role of the ε-greedy strategy.

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## Reinforcement Learning and ε-greedy

Reinforcement Learning (RL) trains an **agent** to choose actions in an **environment**. The environment returns a reward and next state. The goal is to maximize cumulative discounted reward. Key terms: state $s$, action $a$, reward $r$, policy $\pi$, value function and learning rate.

**Exploration** tries actions to learn their outcomes; **exploitation** chooses the currently best-known action. Too much exploration wastes reward; too little may miss better actions.

With ε-greedy, choose a random action with probability $\epsilon$, and the best estimated action with probability $1-\epsilon$. ε may decay as learning progresses.

```mermaid
flowchart TD
S[Observe state] --> D{Random draw < ε?}
D -->|Yes| X[Explore: random action]
D -->|No| E[Exploit: best Q action]
X --> R[Receive reward and next state]
E --> R
R --> U[Update value estimates]
U --> S
```

Reinforcement Learning and ε-greedy

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