Reinforcement Learning / Q Learning Basics with Tic Tac Toe

This summary provides an excellent overview of how to apply Reinforcement Learning (RL) specifically for developing game-playing agents. Here are the key points broken down:

Key Concepts

1. Reinforcement Learning Basics:

   • RL is about learning through interaction with an environment.
   • The agent learns by taking actions, receiving rewards, and updating its policy based on these interactions.

2. Q-Learning Algorithm:

   • Q-learning builds a lookup table (Q-table) to store the expected utility of taking each action in each state.
   • It uses the Bellman equation to update Q-values iteratively: [ Q(s, a) = Q(s, a) + \alpha [R(s,a,s') + \gamma \max_{a'} Q(s', a') - Q(s, a)] ]
   • Here:
     • ( s ): Current state
     • ( a ): Action taken in the current state
     • ( R(s, a, s') ): Reward received after taking action ( a ) from state ( s ), leading to next state ( s' )
     • ( Q(s', a') ): Expected utility of actions

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