3260 papers • 126 benchmarks • 313 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 )
(Image credit: Papersgraph)
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Use these libraries to find game-of-doom models and implementations
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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 and confirms the utility of ViZDoom as an AI research platform and implies that visual reinforcement learning in 3D realistic first- person perspective environments is feasible.
This paper presents the first architecture to tackle 3D environments in first-person shooter games, that involve partially observable states, and substantially outperforms built-in AI agents of the game as well as average humans in deathmatch scenarios.
The proposed model incorporates ideas of traditional filtering-based localization methods, by using a structured belief of the state with multiplicative interactions to propagate belief, and combines it with a policy model to localize accurately while minimizing the number of steps required.
DSR is presented, which generalizes Successor Representations within an end-to-end deep reinforcement learning framework and has several appealing properties including: increased sensitivity to distal reward changes due to factorization of reward and world dynamics, and the ability to extract bottleneck states given successor maps trained under a random policy.
A new version of VizDoom simulator is introduced to create a highly efficient learning environment that provides raw audio observations and studies the performance of different model architectures in a series of tasks that require the agent to recognize sounds and execute instructions given in natural language.
Adding a benchmark result helps the community track progress.