Playing Connect 4 with Deep Q-Learning - Exploring the power of Reinforcement Learning through a well-known game environment
Deep Q-Learning may be one of the most important algorithms in all of Reinforcement Learning as it lacks limitation on the observations it can make and the actions it can take within complex environments. This method of Reinforcement Learning incorporates deep neural networks in a way that allows an agent to ‘play’ an environment repeatedly and learn the environment over time through a system of observations, actions, and rewards. This structure has obvious benefits over a standard deep neural network implementation as it allows the agent to interact with its surroundings, receive feedback from its surroundings, and then optimize for desirable (highly rewarded) future actions. In this article, we will be looking at a familiar environment that was recently instantiated by Kaggle in one of their Kaggle competitions (https://www.kaggle.com/c/connectx).
Before we start exploring the structure of a Deep Q-Learning agent to play Connect 4, let’s first briefly overview the structure of a simple, much less useful Q-Learning agent. The basic idea of Q-Learning is to create a map of the entire observation space, and within this map, record the agents actions. Subsequently, every time the agent encounters the same observation, it will incrementally update the action it previously took based on whether its previous action earned a positive or negative reward. This data structure in which previous actions for each observation in the observation space are stored is known as a Q-Table. This incremental updating of the Q-Table is most commonly done by the following Q-Learning equation:
While this equation appears complicated upon first glance, it is simply using the expected reward to update the specific location of the Q-Table that corresponds to the current observation. Understanding of this process is much better visualized through a code example, though not necessary to work through our Deep Q-Learning project, since we will instead be using a Deep Neural Network to update our “Q-Table” (more on this later).
To better understand the nature of Q-learning, let’s frame an example observation from the perspective of our very well-known environment, Connect 4. The following is the above example state of a Connect 4 board during a game, in other words an element of the observation space (Player 1 chips: 1, Player 2 chips: 2, empty spaces: 0)
Previously shown board observation as seen by the neural network
Here we see a specific arrangement of previously played pieces that would be sent to the agent as an observation. The agent would take this observation and find this observation within its Q-Table. Essentially asking itself the question, “What did I do last time I saw this arrangement of pieces and how can I do better this time?”. It would then proceed to act in the proper manner and throughout the process updating the action in the proper location in the Q-Table for each move. From a broad overview, this seems like a great use case for Q-Learning… other than one subtle exception. Did you catch it?
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