MODEM: ACCELERATING VISUAL MODEL-BASED REINFORCEMENT LEARNING WITH DEMONSTRATIONS

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 160% -250% more successful in completing sparse reward tasks compared to prior approaches in the low data regime (100k interaction steps, 5 demonstrations). Code and videos are available at https://nicklashansen.github.io/modemrl.



Figure 1 . Success rate (%) in sparse reward tasks. Given only 5 human demonstrations and a limited amount of online interaction, our method significantly improves success rate on 21 hard robotics tasks from pixels -including dexterous manipulation, pick-and-place, and locomotion -compared to strong baselines. Reinforcement Learning (RL) provides a principled and complete abstraction for training agents in unknown environments. However, poor sample efficiency of existing algorithms prevent their applicability for real-world tasks like object manipulation with robots. This is further exacerbated in visuo-motor control tasks which present both the challenges of visual representation learning as well as motor control. Model-based RL (MBRL) can in principle (Brafman & Tennenholtz, 2002) improve the sample efficiency of RL by concurrently learning a world model and policy (Ha & Schmidhuber, 2018; Ecoffet et al., 2019; Schrittwieser et al., 2020; Hafner et al., 2020; Hansen et al., 2022a) . Use of imaginary rollouts from the learned model can reduce the need for real environment interactions, and thus improve sample efficiency. However, a series of practical challenges like the difficulty of exploration, the need for shaped rewards, and the need for a high-quality visual representation, prevent MBRL from realizing its full potential. In this work, we seek to overcome these challenges from a practical standpoint, and we propose to do so by using expert demonstrations to accelerate MBRL. Expert demonstrations for visuo-motor control tasks can be collected using human teleoperation, kinesthetic teaching, or scripted policies. While demonstrations provide direct supervision for learning complex behaviors, they can be costly to collect in large quantities (Baker et al., 2022) . However, even a small number of demonstrations can significantly accelerate RL by circumventing challenges related to exploration. Prior works have studied this in the context of model-free RL (MFRL)

