Exploring Deep Recurrent Q-Learning for Navigation in a 3D Environment
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...
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Online Access: | http://eudl.eu/doi/10.4108/eai.16-1-2018.153641 |
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doaj-bb7587eb43cf428993e3a7e21a1490862020-11-25T02:02:37ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Creative Technologies2409-97082018-01-015141510.4108/eai.16-1-2018.153641Exploring Deep Recurrent Q-Learning for Navigation in a 3D EnvironmentRasmus Kongsmar Brejl0Henrik Purwins1Henrik Schoenau-Fog2The Center for Applied Game Research, Department of Architecture, Design, and Media Technology, Technical Faculty of IT and Design, Aalborg University Copenhagen, Denmark; Audio Analysis Lab, Department of Architecture, Design, and Media Technology, Technical Faculty of IT and Design, Aalborg University Copenhagen, Denmark; rasmuskbrejl@gmail.com The Center for Applied Game Research, Department of Architecture, Design, and Media Technology, Technical Faculty of IT and Design, Aalborg University Copenhagen, Denmark; Audio Analysis Lab, Department of Architecture, Design, and Media Technology, Technical Faculty of IT and Design, Aalborg University Copenhagen, DenmarkThe Center for Applied Game Research, Department of Architecture, Design, and Media Technology, Technical Faculty of IT and Design, Aalborg University Copenhagen, DenmarkLearning 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.http://eudl.eu/doi/10.4108/eai.16-1-2018.153641Reinforcement LearningDeep LearningQ-LearningDeep Recurrent Q-LearningArtificial IntelligenceNavigationGame Intelligence |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rasmus Kongsmar Brejl Henrik Purwins Henrik Schoenau-Fog |
spellingShingle |
Rasmus Kongsmar Brejl Henrik Purwins Henrik Schoenau-Fog Exploring Deep Recurrent Q-Learning for Navigation in a 3D Environment EAI Endorsed Transactions on Creative Technologies Reinforcement Learning Deep Learning Q-Learning Deep Recurrent Q-Learning Artificial Intelligence Navigation Game Intelligence |
author_facet |
Rasmus Kongsmar Brejl Henrik Purwins Henrik Schoenau-Fog |
author_sort |
Rasmus Kongsmar Brejl |
title |
Exploring Deep Recurrent Q-Learning for Navigation in a 3D Environment |
title_short |
Exploring Deep Recurrent Q-Learning for Navigation in a 3D Environment |
title_full |
Exploring Deep Recurrent Q-Learning for Navigation in a 3D Environment |
title_fullStr |
Exploring Deep Recurrent Q-Learning for Navigation in a 3D Environment |
title_full_unstemmed |
Exploring Deep Recurrent Q-Learning for Navigation in a 3D Environment |
title_sort |
exploring deep recurrent q-learning for navigation in a 3d environment |
publisher |
European Alliance for Innovation (EAI) |
series |
EAI Endorsed Transactions on Creative Technologies |
issn |
2409-9708 |
publishDate |
2018-01-01 |
description |
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. |
topic |
Reinforcement Learning Deep Learning Q-Learning Deep Recurrent Q-Learning Artificial Intelligence Navigation Game Intelligence |
url |
http://eudl.eu/doi/10.4108/eai.16-1-2018.153641 |
work_keys_str_mv |
AT rasmuskongsmarbrejl exploringdeeprecurrentqlearningfornavigationina3denvironment AT henrikpurwins exploringdeeprecurrentqlearningfornavigationina3denvironment AT henrikschoenaufog exploringdeeprecurrentqlearningfornavigationina3denvironment |
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