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Now it was the reinforcement learning's turn to make a difference. Policies have to be learned to help agents move around a scene, and this is the task of active recognition [44, 48]). The policy will be learned at the same time it is learning other tasks and representation, and it will tell the agent where and how to strategically move to recognize things faster [49, 50].

Results show that policies indeed help the agent to achieve better visual recognition performance, and the agents can strategize their future moves and path for better results that are mostly different from shortest paths [51].

Embodied Question Answering brings QA into the embodied world. The task starts by an agent being spawned at a random location in a 3D environment and asked a question in which its answer can be found somewhere in the environment. For the agent to answer it, it must first strategically navigate to explore the environment, gather necessary data via its vision, and then answer the question when the agent finds it [52, 53].