Timezone: »

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Kurtland Chua · Roberto Calandra · Rowan McAllister · Sergey Levine

Thu Dec 01:20 PM -- 01:25 PM PST @ Room 220 CD

Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance. This is especially true with high-capacity parametric function approximators, such as deep networks. In this paper, we study how to bridge this gap, by employing uncertainty-aware dynamics models. We propose a new algorithm called probabilistic ensembles with trajectory sampling (PETS) that combines uncertainty-aware deep network dynamics models with sampling-based uncertainty propagation. Our comparison to state-of-the-art model-based and model-free deep RL algorithms shows that our approach matches the asymptotic performance of model-free algorithms on several challenging benchmark tasks, while requiring significantly fewer samples (e.g. 8 and 125 times fewer samples than Soft Actor Critic and Proximal Policy Optimization respectively on the half-cheetah task).

Author Information

Kurtland Chua (UC Berkeley)
Roberto Calandra (Facebook AI Research)
Rowan McAllister (UC Berkeley)
Sergey Levine (UC Berkeley)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors