Tag: Reinforcement learning [RL]2020-05-30T11:27:03Z · last modified on 2020-06-10T11:53:21Z
Previously, we saw that reinforcement learning worked quite well on tic-tac-toe. However, there’s something unsatisfying about working with a Q-table storing all the possible states of the game. It feels like the Agent simply memorizes each state of the game and acts according to some memorized rules obtained by its huge amount of experience (remember that the Agent played 10,000,000 games during training). In this second part of the reinforcement learning series, we’ll swap out the Q table for a neural network.2019-11-29T12:12:07Z · last modified on 2020-06-01T11:36:56Z
As a first example to reinforcement learning, we’ll make our computer learn by itself how to play tic-tac-toe. As one of the most simple 2 player games, tic-tac-toe is ideal to get started with reinforcement learning, while still being more interesting that learning to play a single player game.