616,880 research outputs found
Efficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Framework
In this work, we investigate the potential of improving multi-task training
and also leveraging it for transferring in the reinforcement learning setting.
We identify several challenges towards this goal and propose a transferring
approach with a parameter-compositional formulation. We investigate ways to
improve the training of multi-task reinforcement learning which serves as the
foundation for transferring. Then we conduct a number of transferring
experiments on various manipulation tasks. Experimental results demonstrate
that the proposed approach can have improved performance in the multi-task
training stage, and further show effective transferring in terms of both sample
efficiency and performance.Comment: 8 pages, accepted by IEEE Robotics and Automation Letters (RA-L
Sliced Multi-Marginal Optimal Transport
Multi-marginal optimal transport enables one to compare multiple probability
measures, which increasingly finds application in multi-task learning problems.
One practical limitation of multi-marginal transport is computational
scalability in the number of measures, samples and dimensionality. In this
work, we propose a multi-marginal optimal transport paradigm based on random
one-dimensional projections, whose (generalized) distance we term the sliced
multi-marginal Wasserstein distance. To construct this distance, we introduce a
characterization of the one-dimensional multi-marginal Kantorovich problem and
use it to highlight a number of properties of the sliced multi-marginal
Wasserstein distance. In particular, we show that (i) the sliced multi-marginal
Wasserstein distance is a (generalized) metric that induces the same topology
as the standard Wasserstein distance, (ii) it admits a dimension-free sample
complexity, (iii) it is tightly connected with the problem of barycentric
averaging under the sliced-Wasserstein metric. We conclude by illustrating the
sliced multi-marginal Wasserstein on multi-task density estimation and
multi-dynamics reinforcement learning problems
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