Clyde: A Deep Reinforcement Learning DOOM Playing Agent

In many cases games provide noise free computer science at Poznan University. It provides an interface environments and can also encompass the whole world state for AI agents to learn from the raw visual data that is in data structures easily. Much of the early work in this produced by DOOM (Kempka et al. 2016). They also run a domain has focussed on digital implementations of board competition that places these agents into death matches in games, such as backgammon (Tesauro 1995), chess (Campbell, order to compare their performance. A death match in the Hoane, and Hsu 2002) and more recently go (Silver case of this competition is a time limited game mode where et al. 2016). These games have then been used to benchmark each agent must accumulate the highest score possible by many different approaches, including tree search approaches killing other agents in the match. This is where our agent was such as Monte Carlo Tree Search (MCTS) (Browne et al. submitted in order to assess its performance against other 2012) along with other approaches such as deep reinforcement agents.