Meta-Reinforcement Learning in Non-Stationary and Dynamic Environments
In recent years, the subject of deep reinforcement learning (DRL) has developed very rapidly, and is now applied in various fields, such as decision making and control tasks. However, artificial agents trained with RL algorithms require great amounts of training data, unlike humans that are able to...
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| Published in: | IEEE transactions on pattern analysis and machine intelligence Vol. 45; no. 3; pp. 3476 - 3491 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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United States
IEEE
01.03.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0162-8828, 1939-3539, 2160-9292, 1939-3539 |
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| Abstract | In recent years, the subject of deep reinforcement learning (DRL) has developed very rapidly, and is now applied in various fields, such as decision making and control tasks. However, artificial agents trained with RL algorithms require great amounts of training data, unlike humans that are able to learn new skills from very few examples. The concept of meta-reinforcement learning (meta-RL) has been recently proposed to enable agents to learn similar but new skills from a small amount of experience by leveraging a set of tasks with a shared structure. Due to the task representation learning strategy with few-shot adaptation, most recent work is limited to narrow task distributions and stationary environments, where tasks do not change within episodes. In this work, we address those limitations and introduce a training strategy that is applicable to non-stationary environments, as well as a task representation based on Gaussian mixture models to model clustered task distributions. We evaluate our method on several continuous robotic control benchmarks. Compared with state-of-the-art literature that is only applicable to stationary environments with few-shot adaption, our algorithm first achieves competitive asymptotic performance and superior sample efficiency in stationary environments with zero-shot adaption. Second, our algorithm learns to perform successfully in non-stationary settings as well as a continual learning setting, while learning well-structured task representations. Last, our algorithm learns basic distinct behaviors and well-structured task representations in task distributions with multiple qualitatively distinct tasks. |
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| AbstractList | In recent years, the subject of deep reinforcement learning (DRL) has developed very rapidly, and is now applied in various fields, such as decision making and control tasks. However, artificial agents trained with RL algorithms require great amounts of training data, unlike humans that are able to learn new skills from very few examples. The concept of meta-reinforcement learning (meta-RL) has been recently proposed to enable agents to learn similar but new skills from a small amount of experience by leveraging a set of tasks with a shared structure. Due to the task representation learning strategy with few-shot adaptation, most recent work is limited to narrow task distributions and stationary environments, where tasks do not change within episodes. In this work, we address those limitations and introduce a training strategy that is applicable to non-stationary environments, as well as a task representation based on Gaussian mixture models to model clustered task distributions. We evaluate our method on several continuous robotic control benchmarks. Compared with state-of-the-art literature that is only applicable to stationary environments with few-shot adaption, our algorithm first achieves competitive asymptotic performance and superior sample efficiency in stationary environments with zero-shot adaption. Second, our algorithm learns to perform successfully in non-stationary settings as well as a continual learning setting, while learning well-structured task representations. Last, our algorithm learns basic distinct behaviors and well-structured task representations in task distributions with multiple qualitatively distinct tasks.In recent years, the subject of deep reinforcement learning (DRL) has developed very rapidly, and is now applied in various fields, such as decision making and control tasks. However, artificial agents trained with RL algorithms require great amounts of training data, unlike humans that are able to learn new skills from very few examples. The concept of meta-reinforcement learning (meta-RL) has been recently proposed to enable agents to learn similar but new skills from a small amount of experience by leveraging a set of tasks with a shared structure. Due to the task representation learning strategy with few-shot adaptation, most recent work is limited to narrow task distributions and stationary environments, where tasks do not change within episodes. In this work, we address those limitations and introduce a training strategy that is applicable to non-stationary environments, as well as a task representation based on Gaussian mixture models to model clustered task distributions. We evaluate our method on several continuous robotic control benchmarks. Compared with state-of-the-art literature that is only applicable to stationary environments with few-shot adaption, our algorithm first achieves competitive asymptotic performance and superior sample efficiency in stationary environments with zero-shot adaption. Second, our algorithm learns to perform successfully in non-stationary settings as well as a continual learning setting, while learning well-structured task representations. Last, our algorithm learns basic distinct behaviors and well-structured task representations in task distributions with multiple qualitatively distinct tasks. In recent years, the subject of deep reinforcement learning (DRL) has developed very rapidly, and is now applied in various fields, such as decision making and control tasks. However, artificial agents trained with RL algorithms require great amounts of training data, unlike humans that are able to learn new skills from very few examples. The concept of meta-reinforcement learning (meta-RL) has been recently proposed to enable agents to learn similar but new skills from a small amount of experience by leveraging a set of tasks with a shared structure. Due to the task representation learning strategy with few-shot adaptation, most recent work is limited to narrow task distributions and stationary environments, where tasks do not change within episodes. In this work, we address those limitations and introduce a training strategy that is applicable to non-stationary environments, as well as a task representation based on Gaussian mixture models to model clustered task distributions. We evaluate our method on several continuous robotic control benchmarks. Compared with state-of-the-art literature that is only applicable to stationary environments with few-shot adaption, our algorithm first achieves competitive asymptotic performance and superior sample efficiency in stationary environments with zero-shot adaption. Second, our algorithm learns to perform successfully in non-stationary settings as well as a continual learning setting, while learning well-structured task representations. Last, our algorithm learns basic distinct behaviors and well-structured task representations in task distributions with multiple qualitatively distinct tasks. |
| Author | Lerch, David Knoll, Alois Bing, Zhenshan Huang, Kai |
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| References | ref15 Zhang (ref27) Ren (ref9) Schmidhuber (ref2) 1987 Duan (ref3) 2016 Nagabandi (ref12) 2018 Rakelly (ref6) 2019 Wang (ref4) 2016 Mishra (ref13) 2017 Rothfuss (ref16) 2018 Humplik (ref17) 2019 Igl (ref26) 2018 Kingma (ref7) 2013 Gupta (ref23) 2018 Kingma (ref28) 2015 Vaswani (ref21) 2017 van den Oord (ref20) 2016 ref24 Nagabandi (ref11) 2018 Yu (ref14) 2019 (ref1) 2019 Wang (ref10) 2020 Brockman (ref18) 2016 Hausman (ref25) Mnih (ref19) Al-Shedivat (ref22) 2017 Finn (ref5) 2017 Haarnoja (ref8) |
| References_xml | – start-page: 2850 volume-title: Proc. 33rd Int. Conf. Mach. Learn. ident: ref19 article-title: Asynchronous methods for deep reinforcement learning – year: 2019 ident: ref6 article-title: Efficient off-policy meta-reinforcement learning via probabilistic context variables – year: 2018 ident: ref23 article-title: Meta-reinforcement learning of structured exploration strategies – year: 2019 ident: ref1 article-title: Solving Rubiks Cube with a robot hand – year: 2016 ident: ref20 article-title: WaveNet: A generative model for raw audio – year: 2018 ident: ref26 article-title: Deep variational reinforcement learning for POMDPs – year: 2017 ident: ref5 article-title: Model-agnostic meta-learning for fast adaptation of deep networks – year: 2015 ident: ref28 article-title: Variational dropout and the local reparameterization trick – volume-title: Proc. 6th Int. Conf. Learn. Representations ident: ref25 article-title: Learning an embedding space for transferable robot skills – start-page: 2976 volume-title: Proc. 35th Int. Conf. Mach. Learn. ident: ref8 article-title: Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor – year: 2016 ident: ref18 article-title: OpenAI Gym – year: 1987 ident: ref2 article-title: Evolutionary principles in self-referential learning. (On learning how to learn: The meta hook.) – year: 2018 ident: ref11 article-title: Learning to adapt in dynamic, real-world environments through meta-reinforcement learning – year: 2017 ident: ref13 article-title: A simple neural attentive meta-learner – year: 2018 ident: ref16 article-title: ProMP: Proximal meta-policy search – year: 2013 ident: ref7 article-title: Auto-encoding variational bayes – start-page: 12 767 volume-title: Proc. 36th Int. Conf. Mach. Learn. ident: ref27 article-title: LatentGNN: Learning efficient non-local relations for visual recognition – year: 2019 ident: ref17 article-title: Meta reinforcement learning as task inference – year: 2019 ident: ref14 article-title: Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning – volume-title: Proc. Conf. Neural Inf. Process. Syst. ident: ref9 article-title: Context-based meta-reinforcement learning with structured latent space – year: 2017 ident: ref21 article-title: Attention is all you need – year: 2016 ident: ref4 article-title: Learning to reinforcement learn – year: 2018 ident: ref12 article-title: Deep online learning via meta-learning: Continual adaptation for model-based RL – year: 2017 ident: ref22 article-title: Continuous adaptation via meta-learning in nonstationary and competitive environments – ident: ref15 doi: 10.1109/IROS.2012.6386109 – year: 2020 ident: ref10 article-title: Learning context-aware task reasoning for efficient meta-reinforcement learning – year: 2016 ident: ref3 article-title: RL$^{2}$2: Fast reinforcement learning via slow reinforcement learning – ident: ref24 doi: 10.24963/ijcai.2019/387 |
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| SubjectTerms | Adaptation models Agents (artificial intelligence) Algorithms Control tasks Decision making Deep learning Gaussian mixture model Inference algorithms Learning Machine learning Meta-reinforcement learning Multitasking Nonstationary environments Probabilistic models Representations Robot control robotic control Robots Skills Strategy task adaptation Task analysis task inference Training |
| Title | Meta-Reinforcement Learning in Non-Stationary and Dynamic Environments |
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