How is reinforcement learning possible in a high-dimensional world? Without making any assumptions about the struc- ture of the state space, the amount of data required to effec- tively learn a value function grows exponentially with the state space’s dimensionality. However, humans learn to solve high- dimensional problems much more rapidly than would be ex- pected under this scenario. This suggests that humans em- ploy inductive biases to guide (and accelerate) their learning. Here we propose one particular bias—sparsity—that amelio- rates the computational challenges posed by high-dimensional state spaces, and present experimental evidence that humans can exploit sparsity information when it is available.