Behavior Pattern Recognition using A New Representation Model
This work addresses behavior recognition for agents in sequential decision-making problems, but it appears incremental as it builds on established IRL techniques.
The paper tackles behavior pattern recognition by using inverse reinforcement learning (IRL) to learn reward functions from observed sequential decision behavior, and then applies these functions for clustering or classification. Experimental results in GridWorld and the secretary problem show that the method may be superior to existing IRL algorithms and direct feature-based methods for recognition tasks.
We study the use of inverse reinforcement learning (IRL) as a tool for the recognition of agents' behavior on the basis of observation of their sequential decision behavior interacting with the environment. We model the problem faced by the agents as a Markov decision process (MDP) and model the observed behavior of the agents in terms of forward planning for the MDP. We use IRL to learn reward functions and then use these reward functions as the basis for clustering or classification models. Experimental studies with GridWorld, a navigation problem, and the secretary problem, an optimal stopping problem, suggest reward vectors found from IRL can be a good basis for behavior pattern recognition problems. Empirical comparisons of our method with several existing IRL algorithms and with direct methods that use feature statistics observed in state-action space suggest it may be superior for recognition problems.