ProMP: Proximal Meta-Policy Search

Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, Pieter Abbeel

Abstract:

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.

Keywords: Meta-Reinforcement Learning, Meta-Learning, Reinforcement-Learning

TL;DR: A novel and theoretically grounded meta-reinforcement learning algorithm

Code: [ jonasrothfuss/promp](https://github.com/jonasrothfuss/promp) + [ 5 community implementations](https://paperswithcode.com/paper/?openreview=SkxXCi0qFX)

Data: [MuJoCo](https://paperswithcode.com/dataset/mujoco)

Community Implementations: [ 5 code implementations](https://www.catalyzex.com/paper/arxiv:1810.06784/code)