computer replacing my x's tic tac toe unity

It learned to play a bunch of different ATARI games, all from scratch. But this agent was only given raw pixel data – what a human player would see on screen.

Yes, it’s probably easy to write a program that plays “Pong” perfectly if you have access to the underlying game states (position of the ball, paddle, etc). In 2015, Deep Mind made a breakthrough by designing an agent that learns to play video games better than humans. DQN uses a deep neural network to approximate the Q-function, which allows it to handle high-dimensional state spaces. However, in many problems, the state space is too large to be represented by a table, and the Q-function has to be approximated by a function approximator such as a neural network. In traditional Q-learning, the Q-function is updated using the Bellman equation, which is a recursive equation that relates the Q-value of a state-action pair to the Q-values of the next state-action pairs. We represent it by a table where each entry corresponds to the Q-value of a particular state-action pair. We use a Q-function to approximate the true Q-values.

The agent’s goal is to learn the optimal policy, which is the set of actions that maximizes the expected cumulative reward. Q-learning is a type of RL algorithm that is based on the idea of estimating the value of each action in a given state, also known as the Q-value.

But first, let’s understand the theories. In this article, we will go through every type of deep Q-networks with examples in PyTorch. Deep Q-Networks (DQN) algorithm is one of the most popular RL algorithms, which combines Q-learning with deep neural networks.

Computer replacing my x's tic tac toe unity