## LESSWRONGLW

It is an interesting problem to write explicit regret bounds for reinforcement learning with a prior that is the Solomonoff prior or something similar. Of course, any regret bound requires dealing with traps. The simplest approach is, leaving only environments without traps in the prior (there are technical details involved that I won't go into right now). However, after that we are still left with a different major problem. The regret bound we get is very weak. This happens because the prior contains sets of hypotheses of the form "program template augm