Training artificial neural networks to learn a nondeterministic game

It is well known that artificial neural networks (ANNs) can learn deterministic automata. Learning nondeterministic automata is another matter. This is important because much of the world is nondeterministic, taking the form of unpredictable or probabilistic events that must be acted upon. If ANNs are to engage such phenomena, then they must be able to learn how to deal with nondeterminism. In this project the game of Pong poses a nondeterministic environment. The learner is given an incomplete view of the game state and underlying deterministic physics, resulting in a nondeterministic game. Three models were trained and tested on the game: Mona, Elman, and Numenta's NuPIC.Comment: ICAI'15: The 2015 International Conference on Artificial Intelligence, Las Vegas, NV, USA, 201

Single- versus Multiobjective Optimization for Evolution of Neural Controllers in Ms. Pac-Man

The objective of this study is to focus on the automatic generation of game artificial intelligence (AI) controllers for Ms. Pac-Man agent by using artificial neural network (ANN) and multiobjective artificial evolution. The Pareto Archived Evolution Strategy (PAES) is used to generate a Pareto optimal set of ANNs that optimize the conflicting objectives of maximizing Ms. Pac-Man scores (screen-capture mode) and minimizing neural network complexity. This proposed algorithm is called Pareto Archived Evolution Strategy Neural Network or PAESNet. Three different architectures of PAESNet were investigated, namely, PAESNet with fixed number of hidden neurons (PAESNet_F), PAESNet with varied number of hidden neurons (PAESNet_V), and the PAESNet with multiobjective techniques (PAESNet_M). A comparison between the single- versus multiobjective optimization is conducted in both training and testing processes. In general, therefore, it seems that PAESNet_F yielded better results in training phase. But the PAESNet_M successfully reduces the runtime operation and complexity of ANN by minimizing the number of hidden neurons needed in hidden layer and also it provides better generalization capability for controlling the game agent in a nondeterministic and dynamic environment

Playing Cassino with Reinforcement Learning

Reinforcement learning algorithms have been used to create game-playing agents for various games—mostly, deterministic games such as chess, shogi, and Go. This study used Deep-Q reinforcement learning to create an agent that plays a non-deterministic card game, Cassino. This agent’s performance was compared against the performance of a Cassino mobile app. Results showed that the trained models did not perform well and had trouble training around build actions which are important in Cassino. Future research could experiment with other reinforcement learning algorithms to see if they are better at training around build actions

Reinforcement learning and its application to Othello

In this article we describe reinforcement learning, a machine learning technique for solving sequential decision problems. We describe how reinforcement learning can be combined with function approximation to get approximate solutions for problems with very large state spaces. One such problem is the board game Othello, with a state space size of approximately 1028. We apply reinforcement learning to this problem via a computer program that learns a strategy (or policy) for Othello by playing against itself. The reinforcement learning policy is evaluated against two standard strategies taken from the literature with favorable results. We contrast reinforcement learning with standard methods for solving sequential decision problems and give some examples of applications of reinforcement learning in operations research and management science from the literature