Nov 17, 2019 · We present the Working Memory Graph (WMG), an agent that employs multi-head self-attention to reason over a dynamic set of vectors representing observed and ...
We present the Working Memory Graph. (WMG), an agent that employs multi-head self- attention to reason over a dynamic set of vectors representing observed and ...
We present the Working Memory Graph. (WMG), an agent that employs multi-head self- attention to reason over a dynamic set of vectors representing observed and ...
People also ask
What are the 4 components of working memory?
What is 7 +/- 2 working memory?
How do you measure working memory?
What does good working memory look like?
WMG is a Transformer-based RL agent that attends to a dynamic set of vectors representing observed and recurrent state.
This work presents the Working Memory Graph, an agent that employs multi-head self-attention to reason over a dynamic set of vectors representing observed and ...
Jul 1, 2020 · Transformers have increasingly outperformed gated RNNs in obtaining new state-of-the-art results on supervised tasks involving text ...
Jul 13, 2020 · Inspired by this trend, we study the question of how Transformer-based models can improve the performance of sequential decision-making agents.
Sep 2, 2020 · A new neural-based architecture for solving reinforcement learning (RL) problems. WMG uses a deep neural technique developed for natural language processing ...
Nov 17, 2019 · We present the Working Memory Graph (WMG), an agent that employs multi-head self-attention to reason over a dynamic set of vectors representing ...