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 )
Benchmarks
These leaderboards are used to track progress in Game of Doom
| Trend | Dataset | Best Model | Paper | Code | Compare |
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VizDoom
Most implemented papers
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
mwydmuch/ViZDoom
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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
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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
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
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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
We are currently in the process of merging the augmented simulator with the main ViZDoom code repository.
