MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
This addresses the exploration bottleneck for deploying RL in real-world applications, though it is incremental as it builds on existing model-based RL frameworks.
The paper tackled the poor sample efficiency of model-based reinforcement learning in visuo-motor control by incorporating a few demonstrations, resulting in a 150%-250% increase in success rates for sparse reward tasks with only 100K interaction steps and 5 demonstrations.
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). Code and videos are available at: https://nicklashansen.github.io/modemrl