Thank you for the example, I think that illustrates the point well.
Could you help me to understand why you think that more a intelligent agent would be more likely to have a reward-hacking policy that isn’t sharp? The intelligence of the agent should have no bearing on the geometry of the deviations between the reward function and a function representing the true objective. The intelligence of the agent might impact its progression through policies over the course of optimization, and perhaps this difference would result in access to a space of more sophisticated policies that lie in broad, flat optima in the reward function. Is this close to your thinking? I think that criticism amounts to a rejection of the heuristic/intuition that reward-hacking==sharp policy, since this topological feature of the policy space in this problem always existed, regardless of the intelligence of the agent.
This is close to my thinking. Example: landing a plane on an aircraft carrier. Outcomes:
Good landing. +100 points.
Bad landing, pilot dies, carrier damaged. −1,000 points.
Don’t try to land, just eject and ditch the plane safely in the sea. 0 points.
Hypothetical agent is not very smart, with an OODA loop of ten seconds. Attempting a landing is the sharp policy. If the agent makes a mistake in the last ten seconds, it can’t react to fix it, and it crashes. Ejecting is the blunt policy.
(I played a flight simulator as a kid and I never managed to land on the stupid carrier)
Now increase the speed of the agent, so its OODA loop is 0.1 seconds. This makes it 100x smarter by some metrics. Now attempting the landing is a blunt policy, because the agent can recover from mistakes and still stick the landing.
I don’t think this rejects the heuristic. If an agent has a shorter OODA loop then that changes the topology. Also, if an agent can search more of the policy space then even if 99% of reward-hacking policies are sharp, it is more likely to find one of the blunt ones.
Thank you for the example, I think that illustrates the point well.
Could you help me to understand why you think that more a intelligent agent would be more likely to have a reward-hacking policy that isn’t sharp? The intelligence of the agent should have no bearing on the geometry of the deviations between the reward function and a function representing the true objective. The intelligence of the agent might impact its progression through policies over the course of optimization, and perhaps this difference would result in access to a space of more sophisticated policies that lie in broad, flat optima in the reward function. Is this close to your thinking? I think that criticism amounts to a rejection of the heuristic/intuition that reward-hacking==sharp policy, since this topological feature of the policy space in this problem always existed, regardless of the intelligence of the agent.
This is close to my thinking. Example: landing a plane on an aircraft carrier. Outcomes:
Good landing. +100 points.
Bad landing, pilot dies, carrier damaged. −1,000 points.
Don’t try to land, just eject and ditch the plane safely in the sea. 0 points.
Hypothetical agent is not very smart, with an OODA loop of ten seconds. Attempting a landing is the sharp policy. If the agent makes a mistake in the last ten seconds, it can’t react to fix it, and it crashes. Ejecting is the blunt policy.
(I played a flight simulator as a kid and I never managed to land on the
stupidcarrier)Now increase the speed of the agent, so its OODA loop is 0.1 seconds. This makes it 100x smarter by some metrics. Now attempting the landing is a blunt policy, because the agent can recover from mistakes and still stick the landing.
I don’t think this rejects the heuristic. If an agent has a shorter OODA loop then that changes the topology. Also, if an agent can search more of the policy space then even if 99% of reward-hacking policies are sharp, it is more likely to find one of the blunt ones.