Learning distant cause and effect using only local and immediate credit assignment

David Rawlinson; Abdelrahman Ahmed; Gideon Kowadlo

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arXiv · 1905.11589

Learning distant cause and effect using only local and immediate credit assignment

David Rawlinson·Abdelrahman Ahmed·Gideon Kowadlo

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Abstract

We present a recurrent neural network memory that uses sparse coding to create a combinatoric encoding of sequential inputs. Using several examples, we show that the network can associate distant causes and effects in a discrete stochastic process, predict partially-observable higher-order sequences, and enable a DQN agent to navigate a maze by giving it memory. The network uses only biologically-plausible, local and immediate credit assignment. Memory requirements are typically one order of magnitude less than existing LSTM, GRU and autoregressive feed-forward sequence learning models. The most significant limitation of the memory is generalization to unseen input sequences. We explore this limitation by measuring next-word prediction perplexity on the Penn Treebank dataset.

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David Rawlinson, Abdelrahman Ahmed, Gideon Kowadlo. 2019-05-28. Learning distant cause and effect using only local and immediate credit assignment. https://arxiv.org/abs/1905.11589

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