Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood. Existing methods either neglect credit assignment to pre-adaptation behavior or implement it naively. This leads to poor sample-efficiency during meta-training as well as ineffective task identification strategies. This paper provides a theoretical analysis of credit assignment in gradient-based Meta-RL. Building on the gained insights we develop a novel meta-learning algorithm that overcomes both the issue of poor credit assignment and previous difficulties in estimating meta-policy gradients. By controlling the statistical distance of both pre-adaptation and adapted policies during meta-policy search, the proposed algorithm endows efficient and stable meta-learning. Our approach leads to superior pre-adaptation policy behavior and consistently outperforms previous Meta-RL algorithms in sample-efficiency, wall-clock time, and asymptotic performance.
@article{arxiv.1810.06784,
title = {ProMP: Proximal Meta-Policy Search},
author = {Jonas Rothfuss and Dennis Lee and Ignasi Clavera and Tamim Asfour and Pieter Abbeel},
journal= {arXiv preprint arXiv:1810.06784},
year = {2022}
}
Comments
The first three authors contributed equally. Published at ICLR 2019