Deep Reinforcement Learning for Doom using Unsupervised Auxiliary Tasks

Papoudakis, Georgios; Chatzidimitriou, Kyriakos C.; Mitkas, Pericles A.

doi:10.48550/arxiv.1807.01960

Cited by 1 publication

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“…Their approach successfully learned a competitive FPS agent by minimizing a Q-learning objective and showed better performance than average human players. Following this success, more work on learning FPS game agents has been proposed, such as Arnold [29] which benefits from the Action-Navigation architecture, Divide and Conquer deep reinforcement learning [30] which further refined the idea of separating the control strategies of map exploration from enemy combat. Although these methods have shown promising results, their training and evaluation context is largely limited to old-fashioned video games with relatively small world sizes, such as VizDoom (originally 1993) and Quake 3 (originally 1999).…”

Section: Related Workmentioning

confidence: 99%

WILD-SCAV: Benchmarking FPS Gaming AI on Unity3D-based Environments

Chen¹,

Shi²,

Zhao³

et al. 2022

Preprint

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Recent advances in deep reinforcement learning (RL) have demonstrated complex decision-making capabilities in simulation environments such as Arcade Learning Environment [1], MuJoCo [2] and ViZDoom [3]. However, they are hardly extensible to more complicated problems, mainly due to the lack of complexity and variations in the environments they are trained and tested on. Furthermore, they are not extensible to an open world environment to facilitate long-term exploration research. To learn realistic task-solving capabilities, we need to develop an environment with greater diversity and complexity. We developed WILD-SCAV, a powerful and extensible environment based on a 3D open-world FPS (First-Person Shooter) game to bridge the gap. It provides realistic 3D environments of variable complexity, various tasks, and multiple modes of interaction, where agents can learn to perceive 3D environments, navigate and plan, compete and cooperate in a human-like manner. WILD-SCAV also supports different complexities, such as configurable maps with different terrains, building structures and distributions, and multi-agent settings with cooperative and competitive tasks. The experimental results on configurable complexity, multi-tasking, and multi-agent scenarios demonstrate the effectiveness of WILD-SCAV in benchmarking various RL algorithms, as well as it is potential to give rise to intelligent agents with generalized task-solving abilities. The link to our open-sourced code can be found here 1 .

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Section: Related Workmentioning

confidence: 99%