Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Much of recent machine learning has focused on deep learning, in which neural network
weights are trained through variants of stochastic gradient descent. An alternative approach …

A generative recurrent neural network is quickly trained in an unsupervised manner to
model popular reinforcement learning environments through compressed spatio-temporal …

Through the success of deep learning in various domains, artificial neural networks are
currently among the most used artificial intelligence methods. Taking inspiration from the …

The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge
of building AI agents with general competency across dozens of Atari 2600 games. It …

Georgios N. Yannakakis Julian Togelius Page 1 Artificial Intelligence and Games Georgios N.
Yannakakis Julian Togelius Page 2 Artificial Intelligence and Games Page 3 Georgios N …

Reinforcement learning (RL) algorithms update an agent's parameters according to one of
several possible rules, discovered manually through years of research. Automating the …

In this paper, we review recent deep learning advances in the context of how they have
been applied to play different types of video games such as first-person shooters, arcade …

With the development of deep learning, the design of an appropriate network structure
becomes fundamental. In recent years, the successful practice of Neural Architecture Search …

We explore building generative neural network models of popular reinforcement learning
environments. Our world model can be trained quickly in an unsupervised manner to learn a …