Sehoon Ha

TL;DR: Soft Actor-Critic (SAC), the recently introduced off-policy actor-critic algorithm based on the maximum entropy RL framework, achieves state-of-the-art performance, outperforming prior on-policy and off- policy methods in sample-efficiency and asymptotic performance.

TL;DR: This work introduces “control rigs” as an intermediate layer of control module to facilitate the mapping between high-level instructions and low-level control variables, and develops a new sampling-based optimization method, Covariance Matrix Adaptation with Classification (CMA-C), to efficiently compute-control rig parameters.

TL;DR: In this article, a sample-efficient deep RL algorithm based on maximum entropy RL was proposed to learn walking gaits on a real-world minitaur robot in about two hours.

TL;DR: DART (Dynamic Animation and Robotics Toolkit) is a collaborative, cross-platform, open source library that features a multibody dynamic simulator and various kinematic tools for control and motion planning.

TL;DR: A sample-efficient deep RL algorithm based on maximum entropy RL that requires minimal per-task tuning and only a modest number of trials to learn neural network policies is proposed and achieves state-of-the-art performance on simulated benchmarks with a single set of hyperparameters.