  Solving real-life sequential decision making problems under partial
observability involves an exploration-exploitation problem. To be successful,
an agent needs to efficiently gather valuable information about the state of
the world for making rewarding decisions. However, in real-life, acquiring
valuable information is often highly costly, e.g., in the medical domain,
information acquisition might correspond to performing a medical test on a
patient. This poses a significant challenge for the agent to perform optimally
for the task while reducing the cost for information acquisition. In this
paper, we propose a model-based reinforcement learning framework that learns an
active feature acquisition policy to solve the exploration-exploitation problem
during its execution. Key to the success is a novel sequential variational
auto-encoder that learns high-quality representations from partially observed
states, which are then used by the policy to maximize the task reward in a cost
efficient manner. We demonstrate the efficacy of our proposed framework in a
control domain as well as using a medical simulator. In both tasks, our
proposed method outperforms conventional baselines and results in policies with
greater cost efficiency.
