“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …

The first part of a two-part series of papers provides a survey on recent advances in Deep
Reinforcement Learning (DRL) applications for solving partially observable Markov decision …

From the early days of computing, games have been important testbeds for studying how
well machines can do sophisticated decision making. In recent years, machine learning has …

Bayesian methods for machine learning have been widely investigated, yielding principled
methods for incorporating prior information into inference algorithms. In this survey, we …

We present four new reinforcement learning algorithms based on actor-critic and natural-
gradient ideas, and provide their convergence proofs. Actor-critic rein-forcement learning …

Vehicular travel is increasing throughout the world, particularly in large urban areas.
Therefore the need arises for simulating and optimizing traffic control algorithms to better …

TD-Gammon is a neural network that is able to teach itself to play backgammon solely by
playing against itself and learning from the results. Starting from random initial play, TD …

Planning problems are among the most important and well-studied problems in artificial
intelligence. They are most typically solved by tree search algorithms that simulate ahead …

Furthermore, when a set of hand-crafted features is added to the network's input
representation, the result is a truly staggering level of performance: TO-Gammon is now …

Investigation of value based Reinforcement Learning algorithms exhibited a resurgence into
mainstream research in 2015 following demonstration of super-human performance when …