Playing FPS Games with Deep Reinforcement Learning

2016-09-18 · Guillaume Lample, Devendra Singh Chaplot

Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari games, often outperforming humans, using only raw pixels to make their decisions. However, most of these games take place in 2D environments that are fully observable to the agent. In this paper, we present the first architecture to tackle 3D environments in first-person shooter games, that involve partially observable states. Typically, deep reinforcement learning methods only utilize visual input for training. We present a method to augment these models to exploit game feature information such as the presence of enemies or items, during the training phase. Our model is trained to simultaneously learn these features along with minimizing a Q-learning objective, which is shown to dramatically improve the training speed and performance of our agent. Our architecture is also modularized to allow different models to be independently trained for different phases of the game. We show that the proposed architecture substantially outperforms built-in AI agents of the game as well as humans in deathmatch scenarios.

📄 PDF Abstract BibTeX arXiv:1609.05521

Code (7)

glample/Arnold 공식 구현 pytorch

KyleChen400/patch_dqn pytorch

RENHANFEI/patch_sup pytorch

RENHANFEI/vizdoom_health_gathering pytorch

RENHANFEI/vizdoom_patch_dqn pytorch

RENHANFEI/vizdoom_patch_supreme pytorch

jeffchy/Artificial-Idiot tf

Tasks

Deep Reinforcement LearningFPS GamesGame of DoomQ-Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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