Model-based planners for partially observable problems must accommodate both
model uncertainty during planning and goal uncertainty during objective
inference. However, model-based planners may be brittle under these types of
uncertainty because they rely on an exact model and tend to commit to a single
optimal behavior. Inspired by results in the model-free setting, we propose an
entropy-regularized model-based planner for partially observable problems.
Entropy regularization promotes policy robustness for planning and objective
inference by encouraging policies to be no more committed to a single action
than necessary. We evaluate the robustness and objective inference performance
of entropy-regularized policies in three problem domains. Our results show that
entropy-regularized policies outperform non-entropy-regularized baselines in
terms of higher expected returns under modeling errors and higher accuracy
during objective inference.

模型基于策划者在部分可观测问题中必须适应策划过程中的模型和目标不确定性。受无模型设置的结果启发，我们提出了一种熵正则化的模型基于策划者来解决部分可观测问题。通过鼓励策略在规划和目标推理中不过度承诺单一行动，熵正则化提升了策略的鲁棒性和目标推理性能。我们在三个问题领域评估了熵正则化策略的鲁棒性和目标推理性能，并结果显示，在模型错误下的期望回报更高，目标推理中的准确性更高。