One day it can play Zork :)

Very nice paper from famous CSAIL laboratory (sorry for tautology) at MIT:

Narasimhan et al (2015) Language Understanding for Text-based Games Using Deep Reinforcement Learning

In two words: the paper describes a way to teach a machine to perceive game situation through a textual description and make correspondent actions. This is a tough task to do due to complexity of the natural language processing. The algorithm makes use of the Deep Q-Network by Mnih at al (2013 and 2015) and Long-Short Term Memory units. Interestingly an experiment for the transfer learning is also done, that uses networks parameters learned from settings A in the setting B, showing premise of the approach.

Deep reinforcement learning

Cool paper on parallel implementation of a  deep reinforcement learning to play Atari games:

Nair et al (2015) Massively Parallel Methods for Deep Reinforcement Learning

The approach itself, called Deep Q-Networks, is described in these two papers:

1. Mnih et al (2015) Human-level control through deep reinforcement learning
2. Mnih et al (2013) Playing Atari with Deep Reinforcement Learning

The training idea is very generic and human-like: the networks 'sees' a screen and decides which action to take. The action changes the game state, which leads to change of the game screen, etc. Set of actions is different for different games, and what is important is that most (all?) of them are arcades. Because an Atari 2600 emulator is used, the network has enough time to make the computations.