Autonomous Cross-Domain Knowledge Transfer in Lifelong Policy Gradient Reinforcement Learning

By Haitham Bou-Ammar, Eric Eaton, José-Marcio Luna, Paul Ruvolo. 2015

81 citations recorded by the source corpus.

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Citations in the corpus

Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks (2021, background): “Earlier research on lifelong reinforcement learning worked on cross-domain lifelong reinforcement learning, where each domain has a sequence of similar tasks (Wilson et al., 2007; Bou Ammar et al., 2015).”
Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting (2020, background): “For PG-ELLA, we kept the same hyper-parameters as used for LPG-FTW, since they are used in exactly the same way for both methods.”
A Comprehensive Survey on Transfer Learning (2019, background): “Some interesting transfer learning topics are not covered in this survey, such as reinforcement transfer learning [8], lifelong transfer learning [9], and online transfer learning [10].”
Learning on the Job: Online Lifelong and Continual Learning (2020, background): “Some initial but limited work has been done in (Chen and Liu, 2014; Mitchell et al., 2015; Bou Ammar et al, 2015).”
A Bayesian Nonparametic Approach for Estimating Individualized Treatment-Response Curves (2016, methodology): “Others have proposed methods for transfer across tasks (e.g., Li et al. (2009), Ammar et al. (2015)) though often assuming that the responses are modeled at discrete times.”
Towards a Continuous Knowledge Learning Engine for Chatbots (2018, background): “Our work is related to general lifelong learning in (Chen and Liu, 2016; Ruvolo and Eaton, 2013; Chen and Liu, 2014, 2013; Bou Ammar et al., 2015; Shu et al., 2017).”
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey (2020, background): “Such mappings can be specified by hand, or learned automatically (Taylor et al., 2008; Ammar et al., 2015).”
How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition (2022, background): “An extension of this approach supports cross-domain transfer by projecting the dictionary onto domain-specific policy spaces (Bou Ammar et al., 2015), and another extension leverages task descriptors to achieve zero-shot transfer to unseen tasks (Isele et al., 2016; Rostami et al., 2020).”
Learning Transferable Knowledge Through Embedding Spaces (2019, methodology): “The robot arms are simulated using the Robot Toolbox [197].”
Using Task Features for Zero-Shot Knowledge Transfer in Lifelong Learning (2016, background): “More recently, MTL has been extended to a lifelong learning setting, in which tasks arrive consecutively for regression and classification [Ruvolo & Eaton, 2013] and for reinforcement learning [Bou Ammar et al., 2014; 2015].”