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

Thu Dec 6th 04:20 -- 04:25 PM @ Room 220 CD
in Thu Track 1 -- Session 2 »

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)

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