  We present an effective technique for training deep learning agents capable
of negotiating on a set of clauses in a contract agreement using a simple
communication protocol. We use Multi Agent Reinforcement Learning to train both
agents simultaneously as they negotiate with each other in the training
environment. We also model selfish and prosocial behavior to varying degrees in
these agents. Empirical evidence is provided showing consistency in agent
behaviors. We further train a meta agent with a mixture of behaviors by
learning an ensemble of different models using reinforcement learning. Finally,
to ascertain the deployability of the negotiating agents, we conducted
experiments pitting the trained agents against human players. Results
demonstrate that the agents are able to hold their own against human players,
often emerging as winners in the negotiation. Our experiments demonstrate that
the meta agent is able to reasonably emulate human behavior.
