UMASS CS 687 Reinforcement Learning

Project - Evaluating Average-Reward Reinforcement Learning on the Product Delivery Domain.
Report - 687-pdt.pdf

The source files is in the "src" directory. The files are the following.

	env.cpp - Product Delivery Domain Environment
	agent.cpp - Generic Agent class
	QLearningTab.cpp - Agent implementing QLearning with Tabular Representation and Average-Reward
	NLGA.cpp -Non-Learning Greedy Agent (used as benchmark)
	QLearningTLF.cpp - Agent implementing QLearning with Tabular Representation and Average-Reward (not giving good results, so not included in report)

gen_plot.ipynb - IPython Notebook that can be used to generate plots from the csv files generated by running the experiments

Project is built using CLion 

Ref - Scott Proper and Prasad Tadepalli. Scaling model-based average-reward reinforcement learning for
product delivery. In ECML, volume 6, pages 735{742. Springer, 2006.