Game of Doom

5 papers with code • 1 benchmarks • 1 datasets

Doom is an FPS game : the task is typically to train an agent to navigate the game environment, and additionally, acquire points by eliminating enemies.

( Image credit: Playing FPS Games with Deep Reinforcement Learning )


Most implemented papers

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

mwydmuch/ViZDoom 6 May 2016

Here, we propose a novel test-bed platform for reinforcement learning research from raw visual information which employs the first-person perspective in a semi-realistic 3D world.

Playing FPS Games with Deep Reinforcement Learning

glample/Arnold 18 Sep 2016

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.

Deep Successor Reinforcement Learning

Ardavans/DSR 8 Jun 2016

The successor map represents the expected future state occupancy from any given state and the reward predictor maps states to scalar rewards.

Active Neural Localization

devendrachaplot/Neural-Localization ICLR 2018

The results on the 2D environments show the effectiveness of the learned policy in an idealistic setting while results on the 3D environments demonstrate the model's capability of learning the policy and perceptual model jointly from raw-pixel based RGB observations.

Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory Systems

hegde95/Agents_that_Listen 5 Jul 2021

We are currently in the process of merging the augmented simulator with the main ViZDoom code repository.