Yes, and this is a step in the right direction, but as you noted in the writeup, it only applies in a case where we’ve assumed away a number of key problems—among the most critical of which seem to be:
We have an assumed notion of optimality, and I think an implicit assumption that the optimal point is unique, which seems to be needed to define reward—Abram Demski has noted in another post that this is very problematic.
We also need to know a significant amount about both/all agents, and compute expectations in order to design any of their reward functions. That means future agents joining the system could break our agent’s indifference. (As an aside, I’m unclear how we can be sure it is possible to compute rewards in a stable way if their optimal policy can change based on the reward we’re computing.) If we can compute another agent’s reward function when designing our agent, however, we can plausibly hijack that agent.
We also need a reward depending on an expectation of actions, which means we need counterfactuals not only over scenarios, but over the way the other agent reasons. That’s a critical issue I’m still trying to wrap my head around, because it’s unclear to me how a system can reason in those cases.
? Doesn’t the design above do that?
Yes, and this is a step in the right direction, but as you noted in the writeup, it only applies in a case where we’ve assumed away a number of key problems—among the most critical of which seem to be:
We have an assumed notion of optimality, and I think an implicit assumption that the optimal point is unique, which seems to be needed to define reward—Abram Demski has noted in another post that this is very problematic.
We also need to know a significant amount about both/all agents, and compute expectations in order to design any of their reward functions. That means future agents joining the system could break our agent’s indifference. (As an aside, I’m unclear how we can be sure it is possible to compute rewards in a stable way if their optimal policy can change based on the reward we’re computing.) If we can compute another agent’s reward function when designing our agent, however, we can plausibly hijack that agent.
We also need a reward depending on an expectation of actions, which means we need counterfactuals not only over scenarios, but over the way the other agent reasons. That’s a critical issue I’m still trying to wrap my head around, because it’s unclear to me how a system can reason in those cases.