Resumé

Learning to navigate in 3D environments from raw sensory input is an important step towards bridging the gap between human players and artificial intelligence in digital games. Recent advances in deep reinforcement learning have seen success in teaching agents to play Atari 2600 games from raw pixel information where the environment is always fully observable by the agent. This is not true for first-person 3D navigation tasks. Instead, the agent is limited by its field of view which limits its ability to make optimal decisions in the environment. This paper explores using a Deep Recurrent Q-Network implementation with a long short-term memory layer for dealing with such tasks by allowing an agent to process recent frames and gain a memory of the environment. An agent was trained in a 3D first-person labyrinth-like environment for 2 million frames. Informal observations indicate that the trained agent navigated in the right direction but was unable to find the target of the environment.
OriginalsprogEngelsk
Artikelnummere3
BogserieEAI Endrosed Trasactions on Creative Technologies
Vol/bind18
Udgave nummer14
Antal sider5
ISSN2409-9708
DOI
StatusUdgivet - 2018

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    @article{b4f013caa94446acb0ab881eeb91038d,
    title = "Exploring Deep Recurrent Q-Learning for Navigation in a 3D Environment",
    abstract = "Learning to navigate in 3D environments from raw sensory input is an important step towards bridging the gap between human players and artificial intelligence in digital games. Recent advances in deep reinforcement learning have seen success in teaching agents to play Atari 2600 games from raw pixel information where the environment is always fully observable by the agent. This is not true for first-person 3D navigation tasks. Instead, the agent is limited by its field of view which limits its ability to make optimal decisions in the environment. This paper explores using a Deep Recurrent Q-Network implementation with a long short-term memory layer for dealing with such tasks by allowing an agent to process recent frames and gain a memory of the environment. An agent was trained in a 3D first-person labyrinth-like environment for 2 million frames. Informal observations indicate that the trained agent navigated in the right direction but was unable to find the target of the environment.",
    keywords = "Reinforcement Learning, Deep Learning, Q-Learning, Deep Recurrent Q-Learning, Artificial Intelligence, Navigation, Game Intelligence",
    author = "Rasmus Brejl and Hendrik Purwins and Henrik Schoenau-Fog",
    year = "2018",
    doi = "10.4108/eai.16-1-2018.153641",
    language = "English",
    volume = "18",
    journal = "EAI Endrosed Trasactions on Creative Technologies",
    issn = "2409-9708",
    publisher = "EAI - European Alliance for Innovation",
    number = "14",

    }

    Exploring Deep Recurrent Q-Learning for Navigation in a 3D Environment. / Brejl, Rasmus; Purwins, Hendrik; Schoenau-Fog, Henrik .

    I: EAI Endrosed Trasactions on Creative Technologies, Bind 18, Nr. 14, e3, 2018.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

    TY - JOUR

    T1 - Exploring Deep Recurrent Q-Learning for Navigation in a 3D Environment

    AU - Brejl, Rasmus

    AU - Purwins, Hendrik

    AU - Schoenau-Fog, Henrik

    PY - 2018

    Y1 - 2018

    N2 - Learning to navigate in 3D environments from raw sensory input is an important step towards bridging the gap between human players and artificial intelligence in digital games. Recent advances in deep reinforcement learning have seen success in teaching agents to play Atari 2600 games from raw pixel information where the environment is always fully observable by the agent. This is not true for first-person 3D navigation tasks. Instead, the agent is limited by its field of view which limits its ability to make optimal decisions in the environment. This paper explores using a Deep Recurrent Q-Network implementation with a long short-term memory layer for dealing with such tasks by allowing an agent to process recent frames and gain a memory of the environment. An agent was trained in a 3D first-person labyrinth-like environment for 2 million frames. Informal observations indicate that the trained agent navigated in the right direction but was unable to find the target of the environment.

    AB - Learning to navigate in 3D environments from raw sensory input is an important step towards bridging the gap between human players and artificial intelligence in digital games. Recent advances in deep reinforcement learning have seen success in teaching agents to play Atari 2600 games from raw pixel information where the environment is always fully observable by the agent. This is not true for first-person 3D navigation tasks. Instead, the agent is limited by its field of view which limits its ability to make optimal decisions in the environment. This paper explores using a Deep Recurrent Q-Network implementation with a long short-term memory layer for dealing with such tasks by allowing an agent to process recent frames and gain a memory of the environment. An agent was trained in a 3D first-person labyrinth-like environment for 2 million frames. Informal observations indicate that the trained agent navigated in the right direction but was unable to find the target of the environment.

    KW - Reinforcement Learning

    KW - Deep Learning

    KW - Q-Learning

    KW - Deep Recurrent Q-Learning

    KW - Artificial Intelligence

    KW - Navigation

    KW - Game Intelligence

    U2 - 10.4108/eai.16-1-2018.153641

    DO - 10.4108/eai.16-1-2018.153641

    M3 - Journal article

    VL - 18

    JO - EAI Endrosed Trasactions on Creative Technologies

    JF - EAI Endrosed Trasactions on Creative Technologies

    SN - 2409-9708

    IS - 14

    M1 - e3

    ER -