Continual Reinforcement Learning with Complex Synapses
This addresses the problem of continual learning for AI systems, offering an incremental improvement over existing methods.
The paper tackled catastrophic forgetting in neural networks by using a biologically-inspired complex synaptic model in reinforcement learning agents, showing that it mitigates forgetting across tasks and reduces the need for experience replay.
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.