# Publication Articles

#### Feedback Control for Cassie with Deep Reinforcement Learning

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018) Zhaoming Xie (1), Glen Berseth (1), Patrick Clary (2), Jonathan Hurst (2), Michiel van de Panne (1) (1) University of British Columbia (2) Oregon State University Abstract Bipedal locomotion skills are challenging to develop. Control strategies often use local …

#### Model-Based Action Exploration for Learning Dynamic Motion Skills

Abstract Deep reinforcement learning has achieved great strides in solving challenging motion control tasks. Recently, there has been significant work on methods for exploiting the data gathered during training, but there has been less work on how to best generate the data to learn from. For continuous action domains, the …

#### TerrainRL Sim

Abstract We provide $$88$$ challenging simulation environments that range in difficulty. The difficulty in these \environments is linked not only to the number of dimensions in the action space but also to the task complexity. Using more complex and accurate simulations will help push the field closer to creating human-level …if (!document.getElementById('mathjaxscript_pelican_#%@#\$@#')) { var align = "center", indent = "0em", linebreak = "false"; if (false) { align = (screen.width

#### Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control

Abstract Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policies for multiple skills, where each individual skill is a …

#### DeepLoco: Dynamic Locomotion Skills Using Hierarchical Deep Reinforcement Learning

Abstract Learning physics-based locomotion skills is a difficult problem, leading to solutions that typically exploit prior knowledge of various forms. In this paper we aim to learn a variety of environment-aware locomotion skills with a limited amount of prior knowledge. We adopt a two-level hierarchical control framework. First, low-level controllers …