Optimal Policies Tend to Seek Power

Some researchers have speculated that capable reinforcement learning agents are often incentivized to seek resources and power in pursuit of their objectives. While seeking power in order to optimize a misspecified objective, agents might be incentivized to behave in undesirable ways, including rationally preventing deactivation and correction. Others have voiced skepticism: human power-seeking instincts seem idiosyncratic, and these urges need not be present in reinforcement learning agents. We formalize a notion of power within the context of Markov decision processes. With respect to a class of neutral reward function distributions, we provide sufficient conditions for when optimal policies tend to seek power over the environment.