SC²S Colloquium - May 15, 2017

From Sccswiki
Revision as of 14:24, 8 May 2017 by Bakhtiar (talk | contribs) (Created page with "{| class="wikitable" |- | '''Date:''' || May 15, 2017 |- | '''Room:''' || 01.09.014 |- | '''Time:''' || 10:30 am, s.t. |- |} == Ivan Rodriguez: Deep Reinforcement Learning fo...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search
Date: May 15, 2017
Room: 01.09.014
Time: 10:30 am, s.t.

Ivan Rodriguez: Deep Reinforcement Learning for Superhuman Performance in Doom

Over the last years, Reinforcement Learning (RL) has attracted the attention of many researchers. Its successful combination with Deep Neural Networks (DNN), when used as function approximators, has shown to be successful in several works. Rather than target classical RL problems, the most prominent examples of these works develop techniques that allow agents to learn how to play video and board games from raw input data at human level. In this work, we describe the implementation of an algorithm to train an agent for the popular 90's computer game Doom. In particular, we discuss the efforts to improve the efficiency of our approach and the results obtained in several tests scenarios.