Environments for Lifelong Reinforcement Learning.

In this paper, we discuss the desired characteristics of environments that can support the training and evaluation of lifelong reinforcement learning agents, review existing environments from this perspective ... To achieve general artificial intelligence, reinforcement learning (RL) agents should learn not only to optimize returns for one specific task but also to constantly build more complex skills and scaffold ... natural setup for training and evaluating lifelong reinforcement learning agents. ...

arXiv:1811.10732v2 fatcat:wvjiaywe7vde5gvs5eopnihnfy

Multiple Versions

A lifelong reinforcement learning system is a learning system that has the ability to learn through trail-and-error interaction with the environment over its lifetime. ... Some insights into lifelong reinforcement learning are provided, along with a simplistic prototype lifelong reinforcement learning system. ... Acknowledgements The author would like to thank Gaurav Sharma (Borealis AI) for his comments on a draft of the paper. ...

arXiv:2001.09608v1 fatcat:f56hobcawfbbfnxsm3dtscmduy

Open Access

To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). ... Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. ... Transfor- reinforcement learning Shared Model 2G Transfor- reinforcement learning Transfor-reinforcement learning Private Models Shared Model 4G …… …… …… Ability Lifelong learning ...

arXiv:1901.06455v3 fatcat:euoy7yab6bgbno6l7xi6qhpm34

Multiple Versions

and learning efficiently in the new environment. ... We investigate two challenges in such a lifelong learning setting: first, existing off-policy algorithms struggle with the trade-off between being conservative to maintain good performance in the old environment ... OFFLINE DISTILLATION FOR LIFELONG REINFORCEMENT LEARNING In this section, we demonstrate the trade-off between forward transfer and backward transfer in lifelong reinforcement learning and the effectiveness ...

arXiv:2204.05893v1 fatcat:6jlcaw3ddfhfdblymlnghytaqi

We address the latter need by introducing a framework for continual reinforcement-learning development and assessment using Lifelong Learning Explorer (L2Explorer), a new, Unity-based, first-person 3D ... Despite groundbreaking progress in reinforcement learning for robotics, gameplay, and other complex domains, major challenges remain in applying reinforcement learning to the evolving, open-world problems ... Acknowledgments Development of L2Explorer was funded by the DARPA Lifelong Learning Machines (L2M) Program. ...

arXiv:2203.07454v1 fatcat:svjfhofp4vb2da2xzdso7ln6pq

Open Access

Both can be successfully used for open-ended systems and automated learning in unknown environments. ... Lifelong reinforcement learning is a special case of dynamic (process-oriented) reinforcement learning. ... It can be used for distributed multi-task learning [3] , [17] , for automated program discovery [20] , and for lifelong reinforcement learning. ...

doi:10.1109/imcsit.2009.5352802 dblp:conf/imcsit/Nowostawski09 fatcat:fxsrv3ljszdjpkv42dh5hgrwge

This paper presents a multi-agent approach for lifelong machine learning. Verstaevel N., Boes J., Nigon J., d'Amico D. and Gleizes M. Lifelong Machine Learning with Adaptive Multi-Agent Systems. ... These are strong requirements that push the need for lifelong machine learning, where devices can learn models and behaviours during their whole lifetime and are able to transfer them to perform other ... COMBINING SUPERVISED LEARNING AND REINFORCEMENT LEARNING: AN EXPERIMENT WITH A CHILDREN'S ROBOTIC TOY We propose to study the usage of SACL in a Lifelong reinforcement learning problem. ...

doi:10.5220/0006247302750286 dblp:conf/icaart/VerstaevelBNdG17 fatcat:z3bvuxbp3nbdvn5eld4qxvu7py

In this paper, a lifelong learning problem is studied for an Internet of Things (IoT) system. ... To this end, a new lifelong reinforcement learning algorithm, used by the UAV, is proposed in order to adapt the operation of the devices at each visit by the UAV. ... LIFELONG REINFORCEMENT LEARNING Now, we introduce our lifelong reinforcement learning algorithm that merges the concept of knowledge transfer with the policy gradient (PG) framework. ...

arXiv:2103.15374v1 fatcat:b7qjsio5pbb2fbzelrfe73cvqq

We study two state-of-the-art solutions to the multi-agent pickup and delivery (MAPD) problem based on different principles -- multi-agent path-finding (MAPF) and multi-agent reinforcement learning (MARL ... the performance of these algorithms is measured using quite different metrics in their separate lines of work, we aim to benchmark these two methods comprehensively in a simulated warehouse automation environment ... MULTI-AGENT REINFORCEMENT LEARNING Solutions based on multi-agent reinforcement learning (MARL), unlike MAPF-based solutions, do not compute a set full collision-free paths of all the agents in the environment ...

arXiv:2203.07092v1 fatcat:ws3rsevyqrcnpjps6yq34bqr5i

Open Access

In this paper, we propose LifeLong Incremental Reinforcement Learning (LLIRL), a new incremental algorithm for efficient lifelong adaptation to dynamic environments. ... Experiments demonstrate that LLIRL outperforms relevant existing methods, and enables effective incremental adaptation to various dynamic environments for lifelong learning. ... A potential direction for future work would be to employ non-parametric models (e.g., Gaussian process, k-nearest neighbors) to represent the environment with improved flexibility. ...

arXiv:2007.14196v1 fatcat:nrmsbnscvben3hiwch7bm6r4lm

Multiple Versions

While there has been much research conducted in supervised learning domains under lifelong learning, the reinforced lifelong learning domain remains open for much exploration. ... The search for lifelong learning algorithms creates the foundation for this work. ... Summary The previous chapters have, in detail, covered all the proposed mechanisms for enabling lifelong learning agents to learn in reinforcement learning environments, starting from an initial review ...

doi:10.1145/3381755.3381766 dblp:conf/nice/PanditK20 fatcat:flknsyjdprbdxg76upjzpdacju

management, suitable learning environment, and inclusive growth, were extracted from the article data. ... learning, self-directed learning, lifelong learning model, continuing education, nursing education, and lifelong program. ... The learning environment is another underlying requirement for achievement of lifelong learning. ...

doi:10.19082/5541 pmid:29238496 pmcid:PMC5718860 fatcat:6boqplkkmrfupfnnmlahuzroym

DOAJ

Flatland is a simple, lightweight environment for fast prototyping and testing of reinforcement learning agents. It is of lower complexity compared to similar 3D platforms (e.g. ... We propose to use it as an intermediary benchmark for problems related to Lifelong Learning. ... CONCLUSION We propose Flatland, a lightweight, 2-D, partiallyobservable, highly flexible environment for testing and evaluation of reinforcement learning agents, in the context of problems related to Lifelong ...

arXiv:1809.00510v2 fatcat:gqej5j2kdvdxtl6ryhv5devkm4

Multiple Versions

Some traditional hierarchical reinforcement learning techniques enforce this decomposition in a top-down manner, while meta-learning techniques require a task distribution at hand to learn such decompositions ... We perform a series of experiments on high dimensional continuous action control tasks to demonstrate the effectiveness of this approach at both complex single task learning and lifelong learning. ... We are also grateful for the support from Google Cloud in scaling our experiments. ...

arXiv:1903.01567v1 fatcat:tzh5qtcjevhpnnsuhrsqjfqzne

Open Access

In contrast, typical reinforcement learning problem set-ups consider decision processes that are stationary across episodes. ... We further introduce several simulation environments that exhibit lifelong non-stationarity, and empirically find that our approach substantially outperforms approaches that do not reason about environment ... Acknowledgments The authors would like to thank Allan Zhou, Evan Liu, and Laura Smith for helpful feedback on an early version of this paper. Annie Xie was supported by an NSF fellowship. ...

arXiv:2006.10701v1 fatcat:amneuxvwfvejbljeh2236q63xu

Environments for Lifelong Reinforcement Learning [article]

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Some Insights into Lifelong Reinforcement Learning Systems [article]

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Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems [article]

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Offline Distillation for Robot Lifelong Learning with Imbalanced Experience [article]

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L2Explorer: A Lifelong Reinforcement Learning Assessment Environment [article]

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EVM: Lifelong reinforcement and self-learning

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Lifelong Machine Learning with Adaptive Multi-Agent Systems

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Lifelong Learning for Minimizing Age of Information in Internet of Things Networks [article]

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The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications [article]

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Lifelong Incremental Reinforcement Learning with Online Bayesian Inference [article]

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Relational Neurogenesis for Lifelong Learning Agents

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Lifelong learning strategies in nursing: A systematic review

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Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning [article]

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Model Primitive Hierarchical Lifelong Reinforcement Learning [article]

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Deep Reinforcement Learning amidst Lifelong Non-Stationarity [article]

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