Avoiding Wireheading with Value Reinforcement Learning
  How can we design good goals for arbitrarily intelligent agents?
Reinforcement learning (RL) is a natural approach. Unfortunately, RL does not
work well for generally intelligent agents, as RL agents are incentivised to
shortcut the reward sensor for maximum reward -- the so-called wireheading
problem. In this paper we suggest an alternative to RL called value
reinforcement learning (VRL). In VRL, agents use the reward signal to learn a
utility function. The VRL setup allows us to remove the incentive to wirehead
by placing a constraint on the agent's actions. The constraint is defined in
terms of the agent's belief distributions, and does not require an explicit
specification of which actions constitute wireheading.
