Efficient LSTM Training with Eligibility Traces. (arXiv:2209.15502v1 [cs.LG])

Training recurrent neural networks is predominantly achieved via
backpropagation through time (BPTT). However, this algorithm is not an optimal
solution from both a biological and computational perspective. A more efficient
and biologically plausible alternative for BPTT is e-prop. We investigate the
applicability of e-prop to long short-term memorys (LSTMs), for both supervised
and reinforcement learning (RL) tasks. We show that e-prop is a suitable
optimization algorithm for LSTMs by comparing it to BPTT on two benchmarks for
supervised learning. This …

arxiv lstm traces training