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10.1109/ICRA48506.2021.9561219

Meta-reinforcement learning addresses the efficiency and generalization challenges on multi task learning by quickly leveraging the meta-prior policy for a new task.

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10.1145/3411763.3443428

A meta-reinforcement learning user model then simulates the user behaviors when using the design instance upon the simulators.

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10.24963/ijcai.2021/399

Meta-reinforcement learning (RL) has been shown successful for training agents that quickly adapt to related tasks.

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10.5555/3463952.3464131

In this work, we apply meta-reinforcement learning (metaRL) formulation in the context of ad hoc teamwork problem.

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10.1109/TNSM.2021.3057960

Second, a semi-distributed data-driven solution is proposed by developing a novel multi-agent meta-reinforcement learning (MAMRL) framework to solve the formulated problem.

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10.1109/TNNLS.2021.3105407

Meta-reinforcement learning (meta-RL) enables meta-learners to utilize the task-solving skills trained on similar tasks and quickly adapt to new tasks.

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10.1109/TASE.2019.2924444

Hence, this paper undertakes this challenge by integrated meta-reinforcement learning (MRL) of machining parameters to explore the commonalities of optimization models and use the knowledge to respond quickly to new machining tasks.

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10.1007/978-3-030-87583-1_19

The proposed transfer learning strategy re-formulates the original RL problem for task amenability in a meta-reinforcement learning (meta-RL) framework.

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Meta Reinforcement Learning

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Aug 14, 2022

What is/are Meta Reinforcement Learning?

Meta Reinforcement Learning - Meta-reinforcement learning addresses the efficiency and generalization challenges on multi task learning by quickly leveraging the meta-prior policy for a new task. ^[1] A meta-reinforcement learning user model then simulates the user behaviors when using the design instance upon the simulators. ^[2] Meta-reinforcement learning (RL) has been shown successful for training agents that quickly adapt to related tasks. ^[3] In this work, we apply meta-reinforcement learning (metaRL) formulation in the context of ad hoc teamwork problem. ^[4] Second, a semi-distributed data-driven solution is proposed by developing a novel multi-agent meta-reinforcement learning (MAMRL) framework to solve the formulated problem. ^[5] Meta-reinforcement learning (meta-RL) enables meta-learners to utilize the task-solving skills trained on similar tasks and quickly adapt to new tasks. ^[6] Hence, this paper undertakes this challenge by integrated meta-reinforcement learning (MRL) of machining parameters to explore the commonalities of optimization models and use the knowledge to respond quickly to new machining tasks. ^[7] The proposed transfer learning strategy re-formulates the original RL problem for task amenability in a meta-reinforcement learning (meta-RL) framework. ^[8]