Deep Reinforcement Learning For General Video Game Ai

# Deep Reinforcement Learning for General Video Game AI

**arXiv ID:** 1806.02448
**Authors:** Ruben Rodriguez Torrado, Philip Bontrager, Julian Togelius, Jialin Liu, Diego Perez-Liebana
**Published:** 2018-06-06T22:39:26Z
**Abstract:**
The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-specific description language. While the competition has seen plenty of interest, it has so far focused on online planning, providing a forward model that allows the use of algorithms such as Monte Carlo Tree Search.
  In this paper, we describe how we interface GVGAI to the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems. Using this interface, we characterize how widely used implementations of several deep reinforcement learning algorithms fare on a number of GVGAI games. We further analyze the results to provide a first indication of the relative difficulty of these games relative to each other, and relative to those in the Arcade Learning Environment under similar conditions.

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## References

- [arXiv:1806.02448](http://arxiv.org/abs/1806.02448v1)