Intention: should independently be able to demonstrate knowledge of the most basic methods within game AI field and able to reason around its historical development in relation to applications
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Updated
Apr 20, 2017 - Java
Intention: should independently be able to demonstrate knowledge of the most basic methods within game AI field and able to reason around its historical development in relation to applications
Contains exercises from Game AI CS6150 at Northeastern Univ, Boston (Spring 2017)
My personal implementation of the Q-Learning algorithm.
FrozenLake - OpenAI's exercise resolved with Q-learning algorithm
A series of experiments on the performance of Q-Learning Agents in the Dots and Boxes game.
A tic tac toe game in java, which can be trained by machine learning (console & gui).
Implementation of Q-Learning reinforcement learning algorithm with Java programming language.
A Q Learning Model implemented in java used for MIT Battlecode 2023
Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.
CloudSim is primarily a simulation framework for modeling and simulating cloud computing infrastructures and services. While CloudSim itself does not include built-in machine learning capabilities, you can integrate machine learning techniques into CloudSim to optimize various aspects of cloud resource management.
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