Online Planning in POMDPs with Self-Improving Simulators

How can we plan efficiently in a large and complex environment when the time budget is limited? However, there are three main limitations of this "twophase" Given the original simulator of the environment, paradigm, where a simulator is learned offline and which may be computationally very demanding, we then used as-is for online simulation and planning. First, no propose to learn online an approximate but much planning is possible until the offline learning phase finishes, faster simulator that improves over time. To plan which can take a long time. Second, the separation of learning reliably and efficiently while the approximate simulator and planning raises a question on what data collection policy is learning, we develop a method that adaptively should be used during training to ensure good online prediction decides which simulator to use for every simulation, during planning. We empirically demonstrate that when based on a statistic that measures the accuracy the training data is collected by a uniform random policy, the of the approximate simulator. This allows us to learned influence predictors can perform poorly during online use the approximate simulator to replace the original planning, due to distribution shift. Third, completely replacing simulator for faster simulations when it is accurate the original simulator with the approximate one after enough under the current context, thus trading training implies a risk of poor planning performance in certain off simulation speed and accuracy. Experimental situations, which is hard to detect in advance.