I am the creator of the Brown-UMBC Reinforcement Learning and Planning (BURLAP) Java code library, which is for the use and development of single or multi-agent planning and learning algorithms and domains to accompany them. At the core of the library is a rich state and domain representation framework based on the object-oriented MDP (OO-MDP) [1] paradigm that facilitates the creation of discrete, continuous, or relational domains that can consist of any number of different "objects" in the world. Planning and learning algorithms range from classic forward search planning to value function-based stochastic planning and learning algorithms. Also included is a set of analysis tools such as a common framework for the visualization of domains and agent performance in various domains.

For more information, including tutorials and Java documentation, on BURLAP, please see it's official webpage at


or visit the git repository for it at