Opponent Aware Reinforcement Learning

Understand

In certain reinforcement learning (RL) scenarios there are adversaries trying to interfere with the underlying reward process for their own benefit.

• We introduce Threatened Markov Decision Processes (TMDPs) as a framework to support an agent against potential opponents in an RL context as well as schemes resulting in novel learning approaches to deal with TMDPs.
• After introducing our framework and deriving theoretical results, empirical evidence is given via extensive experiments, showing the importance for an RL agent of acknowledging adversarial awareness.