Extracting Strong Policies for Robotics Tasks from Zero-order Trajectory Optimizers

Solving high-dimensional, continuous robotic tasks is a challenging optimization problem. Model-based methods that rely on zero-order optimizers like the cross-entropy method (CEM) have so far shown strong performance and are considered state-of-the-art in the model-based reinforcement learning community. However, this success comes at the cost of high computational complexity, being therefore not suitable for real-time control. In this paper, we propose a technique to jointly optimize the trajectory and distill a policy, which is essential for fast execution in real robotic systems. Our method builds upon standard approaches, like guidance cost and dataset aggregation, and introduces a novel adaptive factor which prevents the optimizer from collapsing to the learner's behavior at the beginning of the training. The extracted policies reach unprecedented performance on challenging tasks as making a humanoid stand up and opening a door without reward shaping.

Publication:
Pinneri*, C., Sawant*, S., Blaes, S., Martius, G. Extracting Strong Policies for Robotics Tasks from Zero-order Trajectory Optimizers In 9th International Conference on Learning Representations (ICLR 2021), May 2021