The Q represents the “quality” of some move given a specific state the following pseudo-code outlines the algorithm: The simplest way to use an agent trained from Q-learning is to pick the action that has the maximum Q-value. Here, our neural network acts as a function approximator for a function Q, where Q(state, action) returns a long-term value of the action given the current state. Perhaps the most common technical approach is Q-learning. The typical setup involves an environment, an agent, states, and rewards.
#BATTLESHEEP HOW TO#
The learning agent learns by interacting with the environment and then figures out how to best map states to actions. There are various technical approaches to deep reinforcement learning, where the idea is to learn a policy that maximizes long-term reward represented numerically. Our experiments focused on how to use the data collected from human players to refine the agent’s ability to play (and win!) against human players. Over time, the automated Battleship-playing agent did better and better, developing strategies to improve its play from game to game.Ĭurrent progress has been made to establish a framework for (1) playing Battleship from random (or user-defined) ship placement (2) deep reinforcement learning from a Deep Q-learner trained from self-play on games starting from randomly positioned ships and (3) collecting data from two-player Battleship games.
#BATTLESHEEP DOWNLOAD#
Ten years after that initial release, Milton Bradley released a computerized version, and now there are numerous online versions that you can play and open source versions that you can download and run yourself.Īt GA-CCRi, we recently built on an open source version to train deep learning neural networks with data from GA-CCRi employees playing Battleship against each other. According to the Wikipedia page for the game Battleship, the Milton Bradley board game has been around since 1967, but it has roots in games dating back to the early 20th century.
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