Tensor low-rank (PARAFAC) policies for Gaussian and softmax policy-gradient methods match neural-network returns on several RL benchmarks while using fewer parameters and converging faster.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Multilinear Tensor Low-Rank Approximation for Policy-Gradient Methods in Reinforcement Learning
Tensor low-rank (PARAFAC) policies for Gaussian and softmax policy-gradient methods match neural-network returns on several RL benchmarks while using fewer parameters and converging faster.