I have just started learning Reinforcement Learning. I was reading a Tic Tac Toe Agent code and what I understood is that basically 2 agents were training by playing 1000s of games and recording various states they discovered during the game and at the end of every game updating the value for the states based on the reward.
So their "learning" was the state values that they had learned throughout all games that they had played. I also ran the agent and discovered that giving the agent a completely new position would cause it to play some random move as it has not seen such a state before.
- So isn't this memorization by random play?
- What happens when an unseen state is seen by the agent? Random move?
- How does AlphaGo or similar such agents handle such a large state space? And how would it react to a new unseen state? It wouldn't be random, right?