The Experts below are selected from a list of 1851 Experts worldwide ranked by ideXlab platform
Pieter Abbeel - One of the best experts on this subject based on the ideXlab platform.
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modular architecture for starcraft ii with deep reinforcement learning
arXiv: Artificial Intelligence, 2018Co-Authors: Haoran Tang, Jeffrey O Zhang, Huazhe Xu, Trevor Darrell, Pieter AbbeelAbstract:We present a novel modular architecture for StarCraft II AI. The architecture splits responsibilities between multiple modules that each control one aspect of the game, such as build-order selection or tactics. A Centralized Scheduler reviews macros suggested by all modules and decides their order of execution. An updater keeps track of environment changes and instantiates macros into series of executable actions. Modules in this framework can be optimized independently or jointly via human design, planning, or reinforcement learning. We apply deep reinforcement learning techniques to training two out of six modules of a modular agent with self-play, achieving 94% or 87% win rates against the "Harder" (level 5) built-in Blizzard bot in Zerg vs. Zerg matches, with or without fog-of-war.
Haoran Tang - One of the best experts on this subject based on the ideXlab platform.
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modular architecture for starcraft ii with deep reinforcement learning
arXiv: Artificial Intelligence, 2018Co-Authors: Haoran Tang, Jeffrey O Zhang, Huazhe Xu, Trevor Darrell, Pieter AbbeelAbstract:We present a novel modular architecture for StarCraft II AI. The architecture splits responsibilities between multiple modules that each control one aspect of the game, such as build-order selection or tactics. A Centralized Scheduler reviews macros suggested by all modules and decides their order of execution. An updater keeps track of environment changes and instantiates macros into series of executable actions. Modules in this framework can be optimized independently or jointly via human design, planning, or reinforcement learning. We apply deep reinforcement learning techniques to training two out of six modules of a modular agent with self-play, achieving 94% or 87% win rates against the "Harder" (level 5) built-in Blizzard bot in Zerg vs. Zerg matches, with or without fog-of-war.
Hao Peng - One of the best experts on this subject based on the ideXlab platform.
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Performance-Aware Speculative Resource Oversubscription for Large-Scale Clusters
IEEE Transactions on Parallel and Distributed Systems, 2020Co-Authors: Renyu Yang, Xiaoyang Sun, Peter Garraghan, Zhenyu Wen, Hao PengAbstract:It is a long-standing challenge to achieve a high degree of resource utilization in cluster scheduling. Resource oversubscription has become a common practice in improving resource utilization and cost reduction. However, current Centralized approaches to oversubscription suffer from the issue with resource mismatch and fail to take into account other performance requirements, e.g., tail latency. In this article we present ROSE, a new resource management platform capable of conducting performance-aware resource oversubscription. ROSE allows latency-sensitive long-running applications (LRAs) to co-exist with computation-intensive batch jobs. Instead of waiting for resource allocation to be confirmed by the Centralized Scheduler, job managers in ROSE can independently request to launch speculative tasks within specific machines according to their suitability for oversubscription. Node agents of those machines can however, avoid any excessive resource oversubscription by means of a mechanism for admission control using multi-resource threshold control and performance-aware resource throttle. Experiments show that in case of mixed co-location of batch jobs and latency-sensitive LRAs, the CPU utilization and the disk utilization can reach 56.34 and 43.49 percent, respectively, but the 95th percentile of read latency in YCSB workloads only increases by 5.4 percent against the case of executing the LRAs alone.
Trevor Darrell - One of the best experts on this subject based on the ideXlab platform.
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modular architecture for starcraft ii with deep reinforcement learning
arXiv: Artificial Intelligence, 2018Co-Authors: Haoran Tang, Jeffrey O Zhang, Huazhe Xu, Trevor Darrell, Pieter AbbeelAbstract:We present a novel modular architecture for StarCraft II AI. The architecture splits responsibilities between multiple modules that each control one aspect of the game, such as build-order selection or tactics. A Centralized Scheduler reviews macros suggested by all modules and decides their order of execution. An updater keeps track of environment changes and instantiates macros into series of executable actions. Modules in this framework can be optimized independently or jointly via human design, planning, or reinforcement learning. We apply deep reinforcement learning techniques to training two out of six modules of a modular agent with self-play, achieving 94% or 87% win rates against the "Harder" (level 5) built-in Blizzard bot in Zerg vs. Zerg matches, with or without fog-of-war.
Jeffrey O Zhang - One of the best experts on this subject based on the ideXlab platform.
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modular architecture for starcraft ii with deep reinforcement learning
arXiv: Artificial Intelligence, 2018Co-Authors: Haoran Tang, Jeffrey O Zhang, Huazhe Xu, Trevor Darrell, Pieter AbbeelAbstract:We present a novel modular architecture for StarCraft II AI. The architecture splits responsibilities between multiple modules that each control one aspect of the game, such as build-order selection or tactics. A Centralized Scheduler reviews macros suggested by all modules and decides their order of execution. An updater keeps track of environment changes and instantiates macros into series of executable actions. Modules in this framework can be optimized independently or jointly via human design, planning, or reinforcement learning. We apply deep reinforcement learning techniques to training two out of six modules of a modular agent with self-play, achieving 94% or 87% win rates against the "Harder" (level 5) built-in Blizzard bot in Zerg vs. Zerg matches, with or without fog-of-war.