Incremental Learning of Planning Actions in Model-Based Reinforcement Learning

Jun Hao Alvin Ng, Ronald P. A. Petrick

Abstract: The soundness and optimality of a plan depends on the correctness of the domain model. Specifying complete domain models can be difficult when interactions between an agent and its environment are complex. We propose a model-based reinforcement learning (MBRL) approach to solve planning problems with unknown models. The model is learned incrementally over episodes using only experiences from the current episode which suits non-stationary environments. We introduce the novel concept of reliability as an intrinsic motivation for MBRL, and a method to learn from failure to prevent repeated instances of similar failures. Our motivation is to improve the learning efficiency and goal-directedness of MBRL. We evaluate our work with experimental results for three planning domains.