Inference of Strategic Behavior based on Incomplete Observation Data
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
|Title of host publication||NIPS17 Workshop: Learning in the Presence of Strategic Behavior|
|Publisher||Carnegie Mellon University|
|Number of pages||4|
|State||Published - 8 Dec 2017|
|MoE publication type||D3 Professional conference proceedings|
|Event||Neural Information Processing Systems - Long Beach, United States|
Duration: 4 Dec 2017 → 9 Dec 2017
Conference number: 31
|Conference||Neural Information Processing Systems|
|Period||04/12/2017 → 09/12/2017|
Inverse reinforcement learning (IRL) is one method for performing this kind of inference based on observations of the agent's behavior.
However, traditional IRL methods are only applicable when the observations are in the form of state-action paths -- an assumption which does not hold in many real-world modelling settings.
This paper demonstrates that inference is possible even with an arbitrary observation noise model.
- Inverse reinforcement learning, Bayesian Inference, Approximate Bayesian computation, Monte Carlo simulation