Inverse Reinforcement Learning with Conditional Choice Probabilities
This work addresses efficiency issues in IRL for robotics and AI applications, though it is incremental by adapting existing econometric methods.
The paper tackles the computational cost of inverse reinforcement learning (IRL) by introducing an algorithm using Conditional Choice Probabilities (CCP) from econometrics, resulting in up to a 5x speedup without compromising reward function quality on standard benchmarks.
We make an important connection to existing results in econometrics to describe an alternative formulation of inverse reinforcement learning (IRL). In particular, we describe an algorithm using Conditional Choice Probabilities (CCP), which are maximum likelihood estimates of the policy estimated from expert demonstrations, to solve the IRL problem. Using the language of structural econometrics, we re-frame the optimal decision problem and introduce an alternative representation of value functions due to (Hotz and Miller 1993). In addition to presenting the theoretical connections that bridge the IRL literature between Economics and Robotics, the use of CCPs also has the practical benefit of reducing the computational cost of solving the IRL problem. Specifically, under the CCP representation, we show how one can avoid repeated calls to the dynamic programming subroutine typically used in IRL. We show via extensive experimentation on standard IRL benchmarks that CCP-IRL is able to outperform MaxEnt-IRL, with as much as a 5x speedup and without compromising on the quality of the recovered reward function.