Optimal Skipping Rates: Training Agents with Fine-Grained Control Using Deep Reinforcement Learning
These days game AI is one of the focused and active research areas in artificial intelligence because computer games are the best test-beds for testing theoretical ideas in AI before practically applying them in real life world. Similarly, ViZDoom is a game artificial intelligence research platform...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2019/2970408 |
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| _version_ | 1832551409407492096 |
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| author | Adil Khan Jiang Feng Shaohui Liu Muhammad Zubair Asghar |
| author_facet | Adil Khan Jiang Feng Shaohui Liu Muhammad Zubair Asghar |
| author_sort | Adil Khan |
| collection | DOAJ |
| description | These days game AI is one of the focused and active research areas in artificial intelligence because computer games are the best test-beds for testing theoretical ideas in AI before practically applying them in real life world. Similarly, ViZDoom is a game artificial intelligence research platform based on Doom used for visual deep reinforcement learning in 3D game environments such as first-person shooters (FPS). While training, the speed of the learning agent greatly depends on the number of frames the agent is permitted to skip. In this paper, how the frame skipping rate influences the agent’s learning and final performance is proposed, particularly using deep Q-learning, experience replay memory, and the ViZDoom Game AI research platform. The agent is trained and tested on Doom’s basic scenario(s) where the results are compared and found to be 10% better compared to the existing state-of-the-art research work on Doom-based agents. The experiments show that the profitable and optimal frame skipping rate falls in the range of 3 to 11 that provides the best balance between the learning speed and the final performance of the agent which exhibits human-like behavior and outperforms an average human player and inbuilt game agents. |
| format | Article |
| id | doaj-art-979434ff503f4520953709d25f223aea |
| institution | Kabale University |
| issn | 1687-9600 1687-9619 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-979434ff503f4520953709d25f223aea2025-02-03T06:01:29ZengWileyJournal of Robotics1687-96001687-96192019-01-01201910.1155/2019/29704082970408Optimal Skipping Rates: Training Agents with Fine-Grained Control Using Deep Reinforcement LearningAdil Khan0Jiang Feng1Shaohui Liu2Muhammad Zubair Asghar3School of Computer Science and Technology, Harbin Institute of Technology, NO. 92, Xidazhi Street, Harbin, Heilongjiang 150001, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, NO. 92, Xidazhi Street, Harbin, Heilongjiang 150001, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, NO. 92, Xidazhi Street, Harbin, Heilongjiang 150001, ChinaInstitute of Computing and Information Technology, Gomal University, D. I. Khan, KP, PakistanThese days game AI is one of the focused and active research areas in artificial intelligence because computer games are the best test-beds for testing theoretical ideas in AI before practically applying them in real life world. Similarly, ViZDoom is a game artificial intelligence research platform based on Doom used for visual deep reinforcement learning in 3D game environments such as first-person shooters (FPS). While training, the speed of the learning agent greatly depends on the number of frames the agent is permitted to skip. In this paper, how the frame skipping rate influences the agent’s learning and final performance is proposed, particularly using deep Q-learning, experience replay memory, and the ViZDoom Game AI research platform. The agent is trained and tested on Doom’s basic scenario(s) where the results are compared and found to be 10% better compared to the existing state-of-the-art research work on Doom-based agents. The experiments show that the profitable and optimal frame skipping rate falls in the range of 3 to 11 that provides the best balance between the learning speed and the final performance of the agent which exhibits human-like behavior and outperforms an average human player and inbuilt game agents.http://dx.doi.org/10.1155/2019/2970408 |
| spellingShingle | Adil Khan Jiang Feng Shaohui Liu Muhammad Zubair Asghar Optimal Skipping Rates: Training Agents with Fine-Grained Control Using Deep Reinforcement Learning Journal of Robotics |
| title | Optimal Skipping Rates: Training Agents with Fine-Grained Control Using Deep Reinforcement Learning |
| title_full | Optimal Skipping Rates: Training Agents with Fine-Grained Control Using Deep Reinforcement Learning |
| title_fullStr | Optimal Skipping Rates: Training Agents with Fine-Grained Control Using Deep Reinforcement Learning |
| title_full_unstemmed | Optimal Skipping Rates: Training Agents with Fine-Grained Control Using Deep Reinforcement Learning |
| title_short | Optimal Skipping Rates: Training Agents with Fine-Grained Control Using Deep Reinforcement Learning |
| title_sort | optimal skipping rates training agents with fine grained control using deep reinforcement learning |
| url | http://dx.doi.org/10.1155/2019/2970408 |
| work_keys_str_mv | AT adilkhan optimalskippingratestrainingagentswithfinegrainedcontrolusingdeepreinforcementlearning AT jiangfeng optimalskippingratestrainingagentswithfinegrainedcontrolusingdeepreinforcementlearning AT shaohuiliu optimalskippingratestrainingagentswithfinegrainedcontrolusingdeepreinforcementlearning AT muhammadzubairasghar optimalskippingratestrainingagentswithfinegrainedcontrolusingdeepreinforcementlearning |