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First-person shooter (FPS) games involve training an agent to navigate a map and eliminate other players.
( Image credit: Procedural Urban Environments for FPS Games )
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ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
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.
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Game of Doom
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Playing FPS Games with Deep Reinforcement Learning
• glample/Arnold
• 
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.
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
In this work we aim to solve this problem by optimizing the efficiency and resource utilization of reinforcement learning algorithms instead of relying on distributed computation.
FPS GAMES GENERAL REINFORCEMENT LEARNING MULTI-AGENT REINFORCEMENT LEARNING
Active Neural Localization
ICLR 2018
• devendrachaplot/Neural-Localization
•
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.
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.
Learning-Driven Exploration for Reinforcement Learning
We introduce entropy-based exploration (EBE) that enables an agent to explore efficiently the unexplored regions of state space.