  There has been a recent surge of interest in developing generally-capable
agents that can adapt to new tasks without additional training in the
environment. Learning world models from reward-free exploration is a promising
approach, and enables policies to be trained using imagined experience for new
tasks. However, achieving a general agent requires robustness across different
environments. In this work, we address the novel problem of generating
curricula in the reward-free setting to train robust world models. We consider
robustness in terms of minimax regret over all environment instantiations and
show that the minimax regret can be connected to minimising the maximum error
in the world model across environment instances. This result informs our
algorithm, WAKER: Weighted Acquisition of Knowledge across Environments for
Robustness. WAKER selects environments for data collection based on the
estimated error of the world model for each environment. Our experiments
demonstrate that WAKER outperforms several baselines, resulting in improved
robustness, efficiency, and generalisation.
