Indifference and compensatory rewards discussion post by Stuart Armstrong 371 days ago | discuss A putative new idea for AI control; index here. It’s occurred to me that there is a framework where we can see all “indifference” results as corrective rewards, both for the utility function change indifference and for the policy change indifference. Imagine that the agent has reward $$R_0$$ and is following policy $$\pi_0$$, and we want to change it to having reward $$R_1$$ and following policy $$\pi_1$$. Then the corrective reward we need to pay it, so that it doesn’t attempt to resist or cause that change, is simply the difference between the two expected values: $$V(R_0|\pi_0)-V(R_1|\pi_1)$$, where $$V$$ is the agent’s own valuation of the expected reward, conditional on the policy. This explains why off-policy reward-based agents are already safely interruptible: since we change the policy, not the reward, $$R_0=R_1$$. And since off-policy agents have value estimates that are indifferent to the policy followed, $$V(R_0|\pi_0)=V(R_1|\pi_1)$$, and the compensatory rewards are zero.

### NEW DISCUSSION POSTS

[Delegative Reinforcement
 by Vadim Kosoy on Stable Pointers to Value II: Environmental Goals | 1 like

Intermediate update: The
 by Alex Appel on Further Progress on a Bayesian Version of Logical ... | 0 likes

Since Briggs [1] shows that
 by 258 on In memoryless Cartesian environments, every UDT po... | 2 likes

This doesn't quite work. The
 by Nisan Stiennon on Logical counterfactuals and differential privacy | 0 likes

I at first didn't understand
 by Sam Eisenstat on An Untrollable Mathematician | 1 like

This is somewhat related to
 by Vadim Kosoy on The set of Logical Inductors is not Convex | 0 likes

This uses logical inductors
 by Abram Demski on The set of Logical Inductors is not Convex | 0 likes

Nice writeup. Is one-boxing
 by Tom Everitt on Smoking Lesion Steelman II | 0 likes

Hi Alex! The definition of
 by Vadim Kosoy on Delegative Inverse Reinforcement Learning | 0 likes

A summary that might be
 by Alex Appel on Delegative Inverse Reinforcement Learning | 1 like

I don't believe that
 by Alex Appel on Delegative Inverse Reinforcement Learning | 0 likes

This is exactly the sort of
 by Stuart Armstrong on Being legible to other agents by committing to usi... | 0 likes

When considering an embedder
 by Jack Gallagher on Where does ADT Go Wrong? | 0 likes

The differences between this
 by Abram Demski on Policy Selection Solves Most Problems | 1 like

Looking "at the very
 by Abram Demski on Policy Selection Solves Most Problems | 0 likes