Incremental Learning of Planning Actions in Model-Based Reinforcement Learning
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.
Cite
Text
Ng and Petrick. "Incremental Learning of Planning Actions in Model-Based Reinforcement Learning." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/443Markdown
[Ng and Petrick. "Incremental Learning of Planning Actions in Model-Based Reinforcement Learning." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/ng2019ijcai-incremental/) doi:10.24963/IJCAI.2019/443BibTeX
@inproceedings{ng2019ijcai-incremental,
title = {{Incremental Learning of Planning Actions in Model-Based Reinforcement Learning}},
author = {Ng, Jun Hao Alvin and Petrick, Ronald P. A.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2019},
pages = {3195-3201},
doi = {10.24963/IJCAI.2019/443},
url = {https://mlanthology.org/ijcai/2019/ng2019ijcai-incremental/}
}