  To understand the goals and goal representations of AI systems, we carefully
study a pretrained reinforcement learning policy that solves mazes by
navigating to a range of target squares. We find this network pursues multiple
context-dependent goals, and we further identify circuits within the network
that correspond to one of these goals. In particular, we identified eleven
channels that track the location of the goal. By modifying these channels,
either with hand-designed interventions or by combining forward passes, we can
partially control the policy. We show that this network contains redundant,
distributed, and retargetable goal representations, shedding light on the
nature of goal-direction in trained policy networks.

Artificial Intelligence