ROBOTICS

REINFORCEMENT LEARNING

MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations

May 04, 2023

Abstract

Poor sample efficiency continues to be the primary challenge for deployment of deep Reinforcement Learning (RL) algorithms for real-world applications, and in particular for visuo-motor control. Model-based RL has the potential to be highly sample efficient by concurrently learning a world model and using synthetic rollouts for planning and policy improvement. However, in practice, sample-efficient learning with model-based RL is bottlenecked by the exploration challenge. In this work, we find that leveraging just a handful of demonstrations can dramatically improve the sample-efficiency of model-based RL. Simply appending demonstrations to the interaction dataset, however, does not suffice. We identify key ingredients for leveraging demonstrations in model learning -- policy pretraining, targeted exploration, and oversampling of demonstration data -- which forms the three phases of our model-based RL framework. We empirically study three complex visuo-motor control domains and find that our method is 150%-250% more successful in completing sparse reward tasks compared to prior approaches in the low data regime (100K interaction steps, 5 demonstrations).

Download the Paper

AUTHORS

Written by

Nicklas Hansen

Yixin Lin

Hao Su

Xiaolong Wang

Vikash Kumar

Aravind Rajeswaran

Publisher

ICLR

Research Topics

Reinforcement Learning

Robotics

Core Machine Learning

Related Publications

July 01, 2024

REINFORCEMENT LEARNING

Behaviour Distillation

Andrei Lupu, Chris Lu, Robert Lange, Jakob Foerster

July 01, 2024

May 06, 2024

REINFORCEMENT LEARNING

COMPUTER VISION

Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint

Haoyue Tang, Tian Xie

May 06, 2024

May 06, 2024

ROBOTICS

Bootstrapping Linear Models for Fast Online Adaptation in Human-Agent Collaboration

Ben Newman, Christopher Paxton, Kris Kitani, Henny Admoni

May 06, 2024

April 30, 2024

REINFORCEMENT LEARNING

Multi-Agent Diagnostics for Robustness via Illuminated Diversity

Mikayel Samvelyan, Minqi Jiang, Davide Paglieri, Jack Parker-Holder, Tim Rocktäschel

April 30, 2024

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.