Bilinear value networks. (arXiv:2204.13695v1 [cs.AI])

The dominant framework for off-policy multi-goal reinforcement learning
involves estimating goal conditioned Q-value function. When learning to achieve
multiple goals, data efficiency is intimately connected with the generalization
of the Q-function to new goals. The de-facto paradigm is to approximate Q(s, a,
g) using monolithic neural networks. To improve the generalization of the
Q-function, we propose a bilinear decomposition that represents the Q-value via
a low-rank approximation in the form of a dot product between two vector
fields. The first …

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