Download burlap JAR file with all dependencies

The Brown-UMBC Reinforcement Learning and Planning (BURLAP) Java code library is for the use and development of single or multi-agent planning and learning algorithms and domains to accompany them. The library supports a wide range of problem types including discrete, continuous, or relational domains. By default states of domains use the rich object-oriented MDP representation, but you can also implement your own forms of state representation. 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.