Unlike humans, who are capable of continual learning over their lifetimes, artificial neural networks have long been known to suffer from a phenomenon known as catastrophic forgetting, whereby new learning can lead to abrupt erasure of previously acquired knowledge. Whereas in a neural network the parameters are typically modelled as scalar values, an individual synapse in the brain comprises a complex network of interacting biochemical components that evolve at different timescales. In this paper, we show that by equipping tabular and deep reinforcement learning agents with a synaptic model that incorporates this biological complexity (Benna & Fusi, 2016), catastrophic forgetting can be mitigated at multiple timescales. In particular, we find that as well as enabling continual learning across sequential training of two simple tasks, it can also be used to overcome within-task forgetting by reducing the need for an experience replay database.
The concept of synaptic consolidation has been applied in a number of recent works that tackle the continual learning problem by adding quadratic terms to the cost function that selectively penalise moving parameters according to how important they are for the recall of previous tasks @cite_21 @cite_0 @cite_16 @cite_25 . In @cite_0 , the importance of each parameter for a task is proportional to its term in the diagonal of the Fisher Information matrix at the end of training. In @cite_16 , the importance factor for a parameter is calculated in an online fashion by determining its contribution to the drop in the loss function over training; in contrast to @cite_0 , which uses a local approximation of importance, this method provides a more global interpretation by considering a parameter's impact over the whole learning trajectory. In @cite_25 , importance is made proportional to the derivative of the L2-norm of the network output with respect to each parameter; by not relying on the loss function for consolidation, their method can be flexibly used to constrain the model with different data than those that were trained on.
@cite_0 @cite_21 @cite_25 @cite_16
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Abstract The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially. Humans can learn in a continuous manner. Old rarely utilized knowledge can be overwritten by new incoming information while important, frequently used knowledge is prevented from being erased. In artificial learning systems, lifelong learning so far has focused mainly on accumulating knowledge over tasks and overcoming catastrophic forgetting. In this paper, we argue that, given the limited model capacity and the unlimited new information to be learned, knowledge has to be preserved or erased selectively. Inspired by neuroplasticity, we propose an online method to compute the importance of the parameters of a neural network, based on the data that the network is actively applied to, in an unsupervised manner. After learning a task, whenever a sample is fed to the network, we accumulate an importance measure for each parameter of the network, based on how sensitive the predicted output is to a change in this parameter. When learning a new task, changes to important parameters are penalized. We show that a local version of our method is a direct application of Hebb's rule in identifying the important connections between neurons. We test our method on a sequence of object recognition tasks and on the challenging problem of learning an embedding in a continuous manner. We show state of the art performance and the ability to adapt the importance of the parameters towards what the network needs (not) to forget, which may be different for different test conditions. While deep learning has led to remarkable advances across diverse applications, it struggles in domains where the data distribution changes over the course of learning. In stark contrast, biological neural networks continually adapt to changing domains, possibly by leveraging complex molecular machinery to solve many tasks simultaneously. In this study, we introduce intelligent synapses that bring some of this biological complexity into artificial neural networks. Each synapse accumulates task relevant information over time, and exploits this information to rapidly store new memories without forgetting old ones. We evaluate our approach on continual learning of classification tasks, and show that it dramatically reduces forgetting while maintaining computational efficiency.