2013/07/14 by Aristide C. Y. Tossou, Tossou, Aristide C. Y., Christos Dimitrakakis +1 · 1 citation
Computer Science · Engineering · Mathematics · #Advanced Control Systems Optimization #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reinforcement Learning in Robotics #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1307.3785
UAI 2013
arxiv created 2013/07/14 · openalex publication_date 2013/07/14 · arxiv updated 2013/07/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider the problem of learning by demonstration from agents acting in unknown stochastic Markov environments or games. Our aim is to estimate agent preferences in order to construct improved policies for the same task that the agents are trying to solve. To do so, we extend previous probabilistic approaches for inverse reinforcement learning in known MDPs to the case of unknown dynamics or opponents. We do this by deriving two simplified probabilistic models of the demonstrator's policy and utility. For tractability, we use maximum a posteriori estimation rather than full Bayesian inference. Under a flat prior, this results in a convex optimisation problem. We find that the resulting algorithms are highly competitive against a variety of other methods for inverse reinforcement learning that do have knowledge of the dynamics.