Spatial representations that transfer by remapping emerge from meta-reinforcement learning

Animals and artificial agents navigating partially observable environments construct spatial representations from limited egocentric input. In artificial agents and in models of the hippocampal cognitive map alike, such representations are typically learned with predictive or auxiliary objectives decoupled from reward, then treated as a fixed basis for value learning. Spatial representations can emerge from reward alone, but how they are organised to transfer across environments is unknown. Here we train a recurrent agent with meta-reinforcement learning across grid-worlds with varying home and reward locations, using no spatial priors or auxiliary objectives, and analyse its hidden units as a neural population. With weights fixed, the agent solves novel environments in-context, returning home by a shortest path on $>$99% of trajectories, even with sensory cues removed. Its hidden states form a spatial representation: units develop localised fields, position is linearly decodable, and hidden-state similarity tracks spatial distance when the agent navigates from memory but not when its goal is in view. Across unmarked home locations this representation remaps selectively, with a subset of units tracking the home while others maintain a largely stable spatial code. These remapping units determine where the agent goes: ablating them degrades home decoding far more than ablating any other units, and overwriting their state once, with the pattern for a different home, sends it to that false home. These properties resemble the hippocampus, where dedicated cells follow a remembered goal while the rest of the map is largely unchanged, and which is required for navigation from memory but not towards a visible goal. Reward maximisation alone thus yields a spatial representation that transfers across environments, so a spatial basis need neither be fixed in advance nor relearned in new environments.