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meta reinforcement learning 10.1145/3449639.3459386

Thereafter, we propose an evolutionary meta reinforcement learning approach to search for an initial policy that can quickly adapt to the upcoming target tasks.

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meta reinforcement learning 10.1145/3447548.3467242

In spite of the success of existing meta reinforcement learning methods, they still have difficulty in learning a meta policy effectively for RL problems with sparse reward.

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10.1145/3449639.3459386

Thereafter, we propose an evolutionary meta reinforcement learning approach to search for an initial policy that can quickly adapt to the upcoming target tasks.

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More Meta Reinforcement sentence examples

10.1145/3447548.3467242

In spite of the success of existing meta reinforcement learning methods, they still have difficulty in learning a meta policy effectively for RL problems with sparse reward.

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10.1109/ICASSP39728.2021.9413446

Experimental results on meta reinforcement learning environments demonstrate a substantial performance gain as well as being less sensitive to hyper-parameter choice and robust to distribution mismatch.

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10.13140/RG.2.2.35699.45609

We apply the meta reinforcement learning framework to optimize an integrated and adaptive guidance and flight control system for an air-to-air missile, implementing the system as a deep neural network (the policy).

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10.1109/TPDS.2020.3014896

To overcome this weakness, we propose a task offloading method based on meta reinforcement learning, which can adapt fast to new environments with a small number of gradient updates and samples.

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10.1109/TNSM.2021.3087258

To tackle these challenges, we propose a Deep Meta Reinforcement Learning-based Offloading (DMRO) algorithm, which combines multiple parallel DNNs with Q-learning to make fine-grained offloading decisions.

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10.1109/itsc48978.2021.9564972

We formulate this challenge as a multi-Markov Decision Processes (MDPs) adaptation problem and developed Meta Reinforcement Learning (MRL) driving policies to showcase their quick learning capability.

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10.1115/1.4049531

We propose a meta reinforcement learning framework to enhance the adaptability of collaborative robots to new tasks through task modularization and efficient transfer of policies from previously learned task modules.

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10.1109/IJCNN52387.2021.9534316

Meta Reinforcement Learning (MRL) enables an agent to learn from a limited number of past trajectories and extrapolate to a new task.

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10.18653/v1/2021.metanlp-1.1

In this paper, we propose a meta reinforcement learning based method to train text agents through learning-to-explore.

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Meta Reinforcement

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Aug 14, 2022

What is/are Meta Reinforcement?

Meta Reinforcement - Thereafter, we propose an evolutionary meta reinforcement learning approach to search for an initial policy that can quickly adapt to the upcoming target tasks. ^[1] In spite of the success of existing meta reinforcement learning methods, they still have difficulty in learning a meta policy effectively for RL problems with sparse reward. ^[2] Experimental results on meta reinforcement learning environments demonstrate a substantial performance gain as well as being less sensitive to hyper-parameter choice and robust to distribution mismatch. ^[3] We apply the meta reinforcement learning framework to optimize an integrated and adaptive guidance and flight control system for an air-to-air missile, implementing the system as a deep neural network (the policy). ^[4] To overcome this weakness, we propose a task offloading method based on meta reinforcement learning, which can adapt fast to new environments with a small number of gradient updates and samples. ^[5] To tackle these challenges, we propose a Deep Meta Reinforcement Learning-based Offloading (DMRO) algorithm, which combines multiple parallel DNNs with Q-learning to make fine-grained offloading decisions. ^[6] We formulate this challenge as a multi-Markov Decision Processes (MDPs) adaptation problem and developed Meta Reinforcement Learning (MRL) driving policies to showcase their quick learning capability. ^[7] We propose a meta reinforcement learning framework to enhance the adaptability of collaborative robots to new tasks through task modularization and efficient transfer of policies from previously learned task modules. ^[8] Meta Reinforcement Learning (MRL) enables an agent to learn from a limited number of past trajectories and extrapolate to a new task. ^[9] In this paper, we propose a meta reinforcement learning based method to train text agents through learning-to-explore. ^[10]

meta reinforcement learning

Thereafter, we propose an evolutionary meta reinforcement learning approach to search for an initial policy that can quickly adapt to the upcoming target tasks. ^[1] In spite of the success of existing meta reinforcement learning methods, they still have difficulty in learning a meta policy effectively for RL problems with sparse reward. ^[2] Experimental results on meta reinforcement learning environments demonstrate a substantial performance gain as well as being less sensitive to hyper-parameter choice and robust to distribution mismatch. ^[3] We apply the meta reinforcement learning framework to optimize an integrated and adaptive guidance and flight control system for an air-to-air missile, implementing the system as a deep neural network (the policy). ^[4] To overcome this weakness, we propose a task offloading method based on meta reinforcement learning, which can adapt fast to new environments with a small number of gradient updates and samples. ^[5] To tackle these challenges, we propose a Deep Meta Reinforcement Learning-based Offloading (DMRO) algorithm, which combines multiple parallel DNNs with Q-learning to make fine-grained offloading decisions. ^[6] We formulate this challenge as a multi-Markov Decision Processes (MDPs) adaptation problem and developed Meta Reinforcement Learning (MRL) driving policies to showcase their quick learning capability. ^[7] We propose a meta reinforcement learning framework to enhance the adaptability of collaborative robots to new tasks through task modularization and efficient transfer of policies from previously learned task modules. ^[8] Meta Reinforcement Learning (MRL) enables an agent to learn from a limited number of past trajectories and extrapolate to a new task. ^[9] In this paper, we propose a meta reinforcement learning based method to train text agents through learning-to-explore. ^[10]