Reinforcement Learning Cheat Sheet (Exam Killer Version)
*1. Core Idea (Write This in Any Answer Intro)
*
Reinforcement Learning is a learning paradigm where an agent interacts with an environment and learns to take actions that maximize cumulative reward over time.

Keywords to include:

Trial and error
Reward signal
Sequential decision making
2. RL Framework (Must Draw in Exam)

Agent → Action → Environment → Reward → New State

Write:

Agent (decision maker)
Environment (external system)
State (current situation)
Action (choice)
Reward (feedback)

👉 Example (very important for marks):

Game playing / robot navigation
** 3. Markov Decision Process (MDP)**

Definition:
MDP is a mathematical model for RL problems.

Tuple:
(S, A, P, R, γ)

S → States
A → Actions
P → Transition probability
R → Reward
γ → Discount factor

👉 Key concept:
Markov Property → Future depends only on present state

4. Return & Discount Factor

Return = total future reward

γ (0 to 1)
High γ → future matters
Low γ → immediate reward matters
5. Value Functions (Very Important)
State Value: V(s) → how good a state is
Action Value: Q(s,a) → how good an action is

👉 Always mention:
“Expected cumulative reward”

6. Bellman Equation (CORE CONCEPT)

👉 Key idea:

Breaks problem into smaller subproblems
Recursive nature
7. Policy

Policy = strategy of agent

Deterministic → fixed action
Stochastic → probability-based
👉 Write:
π(a|s)

8. Q-Learning (Most Important Algorithm)

Off-policy
Uses max future reward
9. SARSA

On-policy
Uses actual next action
10. Q-Learning vs SARSA (Exam Favorite)

11. Exploration vs Exploitation
Exploration → try new actions
Exploitation → use best known

👉 Method:
Epsilon-greedy
12. Monte Carlo vs TD Learning

13. Policy Iteration vs Value Iteration
Policy Iteration:
Evaluate → Improve
Value Iteration:
Directly update values
14. Common Exam Mistakes (Avoid These)
Writing definitions without examples
Skipping diagrams
Not explaining formulas
No comparison tables
15. 1-Minute Revision Strategy

Before exam Revise:
Bellman Equation
Q-Learning & SARSA
MDP

👉 These alone can cover most paper.
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