#reinforcement-learning
As one can see, the types of generalisation provided by UVFAs and SF&GPI are in some sense complementary. It is then natural to ask if we can simultaneously have the two types of generalisation. In this paper we propose a model that provides exactly that. The main insight is actually simple: since SFs are multi-dimensional value functions, we can extend them in the same way as universal value functions extend regular value functions. In the next section we elaborate on how exactly to do so.
If you want to change selection, open document below and click on "Move attachment"
pdf
owner:
reseal - (no access) - Universal Successor Features Approximators, p4
Summary
status | not read | | reprioritisations | |
---|
last reprioritisation on | | | suggested re-reading day | |
---|
started reading on | | | finished reading on | |
---|
Details