Used openai's gym toolkit

5 Important things for Reinforcement Learning:

1. Environment 🎮
2. Agent 👤
3. States 🎰
4. Action 🎲
5. Reward 🥇

Environment of FrozenLake problem:
SFFF
FHFH
FFFH
HFFG

• S stands for Starting position, safe
• F stands for frozen lake which is walkable, safe
• H stands for Hole in frozen lake, fail
• G stands for Goal

The Agent will explore the different ways to reach the goal and is rewarded for finding a walkable path to a goal.

This is the equation to feed values in Qtable:
Q[state, action] = Q[state, action] + LEARNING_RATE * (reward + GAMMA * np.max(Q[next_state, :]) - Q[state, action])

Qtable contains the transitional values of between states and action.