Scalable Recollections for Continual Lifelong Learning

نویسندگان

  • Matthew Riemer
  • Tim Klinger
  • Michele Franceschini
  • Djallel Bouneffouf
چکیده

Given the recent success of Deep Learning applied to a variety of single tasks, it is natural to consider more human-realistic settings. Perhaps the most difficult of these settings is that of continual lifelong learning, where the model must learn online over a continuous stream of non-stationary data. A continual lifelong learning system must have three primary capabilities to succeed: it must learn and adapt over time, it must not forget what it has learned, and it must be efficient in both training time and memory. Recent techniques have focused their efforts largely on the first two capabilities while the third capability remains largely unexplored. In this paper, we consider the problem of efficient and effective storage of experiences over very large time-frames. In particular we consider the case where typical experiences are n bits and memories are limited to k bits for k << n. We present a novel scalable architecture and training algorithm in this challenging domain and provide an extensive evaluation of its performance. Our results show that we can achieve considerable gains on top of state-of-the-art methods such as GEM.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Continual Lifelong Learning with Neural Networks: A Review

Humans and animals have the ability to continually acquire and fine-tune knowledge throughout their lifespan. This ability is mediated by a rich set of neurocognitive functions that together contribute to the early development and experiencedriven specialization of our sensorimotor skills. Consequently, the ability to learn from continuous streams of information is crucial for computational lea...

متن کامل

The Importance of Selective Knowledge Transfer for Lifelong Learning

As knowledge transfer research progresses from single transfer to lifelong learning scenarios, it becomes increasingly important to properly select the source knowledge that would best transfer to the target task. In this position paper, we describe our previous work on selective knowledge transfer and relate it to problems in lifelong learning. We also briefly discuss our ongoing work to devel...

متن کامل

Recurrent Transition Hierarchies for Continual Learning: A General Overview

Continual learning is the unending process of learning new things on top of what has already been learned (Ring 1994). Temporal Transition Hierarchies (TTHs) were developed to allow prediction of Markov-k sequences in a way that was consistent with the needs of a continual-learning agent (Ring 1993). However, the algorithm could not learn arbitrary temporal contingencies. This paper describes R...

متن کامل

Machine Lifelong Learning: Challenges and Benefits for Artificial General Intelligence

We propose that it is appropriate to more seriously consider the nature of systems that are capable of learning over a lifetime. There are three reasons for taking this position. First, there exists a body of related work for this research under names such as constructive induction, continual learning, sequential task learning and most recently learning with deep architectures. Second, the comp...

متن کامل

Scalable lifelong reinforcement learning

Lifelong reinforcement learning provides a successful framework for agents to learn multiple consecutive tasks sequentially. Current methods, however, suffer from scalability issues when the agent has to solve a large number of tasks. In this paper, we remedy the above drawbacks and propose a novel scalable technique for lifelong reinforcement learning. We derive an algorithm which assumes the ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017