These skills are refined and updated through an iterative comparison strategy, enabling efficient adaptation to unseen environments.

We demonstrate the versatility of our approach for both fully data-driven and for physics-aware neural solvers.

Neural Information Processing Systems http://nips.cc/

Vision-and-Language Navigation requires the agent to follow language instructions to navigate through 3D environments.

Directional features As in previous work [3, 6, 10], we apply a 128-dimensional directional encoding byreplicating(cosθi,sinθi,cosφi,sinφi)by32times torepresent theorientation ofeach single-viewiwith respect to the agent's current orientation, whereθi andφi are the angles of the heading and elevation to that single-view. Replicating the encoding by 32 times does not enrich its information but makes its gradient 32 times larger during back-propagation. We suspect that this benefits the agent to learn about the action-related terms (e.g.

We incorporate an exogenous variable in the SCM that is learned and utilised for reasoning about interventionsintheobservation.

Aprominent challenge is to train an agent capable of generalising to new environments attest time, rather than one that simply memorises trajectories and visual details observed during training. We propose a new learning strategy that learns both from observations and generatedcounterfactual environments.