Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning . This method, termed "Actor-Mimic", exploits the use of deep reinforcement learning and model compression techniques to train a single policy network that learns how to act in a.
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This method, termed "Actor-Mimic", exploits the use of deep reinforcement learning and model compression techniques to train a single policy network that learns how to act in a.
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The ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent. Towards this goal, we define a novel.
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Towards this goal, we define a novel method of multitask and transfer learning that enables an autonomous agent to learn how to behave in multiple tasks simultaneously, and.
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[7] E. Parisotto, L. J. Ba, and R. Salakhutdinov, “Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning,” in ICLR, 2016. [8] J. L. McClelland, B. L. McNaughton, and R..
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Learning to Reweight Examples for Robust Deep Learning Reconciling modern machine learning and the bias-variance trade-off Drug repurposing through joint learning on knowledge graphs.
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The ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent. Towards this goal, we.
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The ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent. Towards this goal, we define a novel.
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For example, in (Parisotto et al., 2016), the proposed actor-mimic method combines deep reinforcement learning with model-compression techniques to train a policy network that.
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In this paper we investigate two hypothesis regarding the use of deep reinforcement learning in multiple tasks. The first hypothesis is driven by the question of whether a deep reinforcement.
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View ACTOR-MIMIC DEEP MULTITASK AND TRANSFER REINFORCEMENT LEARNING_20180118194149.pdf from CIS 579 at University of Michigan, Dearborn. Published.
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Figure 2: The Actor-Mimic, expert DQN, and Multitask DQN (MDQN) training curves for 40 training epochs for each of the 8 games. A training epoch is 250,000 frames and for each training.
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Distillation for Multi-Task Transfer Parisotto et al. Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning _ some other details (e.g., feature regression objective) –see paper.
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Towards this goal, we define a novel method of multitask and transfer learning that enables an autonomous agent to learn how to behave in multiple tasks simultaneously, and.
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AI-ON / Multitask-and-Transfer-Learning Public. Notifications Fork 32; Star 144. Code; Issues 10; Pull requests 7;. Deep Multitask and Transfer Reinforcement Learning: #9..
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In the following experiments, we validate the Actor-Mimic method by demonstrating its effectiveness at both multitask and transfer learning in the Arcade Learning Environment.
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Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning. This method, termed Actor-Mimic, exploits the use of deep reinforcement learning and model compression.
Source: image.slidesharecdn.com
The ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent.... Skip to main content.
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Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning. eparisotto/ActorMimic • • 19 Nov 2015. The ability to act in multiple environments and transfer.
