Second, a system can be an “optimizer” in the sense that it optimizes its environment. A human is an optimizer in this sense, because we robustly take actions that push our environment in a certain direction. A reinforcement learning agent can also be thought of as an optimizer in this sense, but confined to whatever environment it is run in.
This definition of optimizer_2 depends on the definition of “environment”. It seems that for an RL agent you use the word “environment” to mean the formal environment as defined in RL. How do you define “environment”, for this purpose, in non-RL settings?
What should be considered the environment of a SAT solver, or an arbitrary mesa-optimizer that was optimized to be a SAT solver?
This definition of optimizer_2 depends on the definition of “environment”. It seems that for an RL agent you use the word “environment” to mean the formal environment as defined in RL. How do you define “environment”, for this purpose, in non-RL settings?
What should be considered the environment of a SAT solver, or an arbitrary mesa-optimizer that was optimized to be a SAT solver?