Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards Published 2018-09-27 Download video MP4 360p Recommendations 02:53 Adaptive Prior Selection for Repertoire-based Online Adaptation in Robotics 01:26 Learning to grasp with a jamming gripper and the Black-DROPS algorithm 01:42 Soft Tensegrity Robots 03:23 Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors 02:19 Reset-Free Trial and Error for Robot Damage Recovery 03:58 Learning and adapting quadruped gaits with the "Intelligent Trial & Error'' algorithm. 02:57 Adaptive Prior Selection for Repertoire-based Online Learning in Robotics 01:15 Minimally invasive exploration for heritage buildings 01:28 First do not fall: learning to exploit a wall with a damaged humanoid robot 00:44 Trial-and-Error Learning of Repulsors for Humanoid QP-based Whole-Body Control 09:28 James Webb Telescope Just Announced First Real Image Of Massive Structure In Space 00:57 Example of the U-Chain algorithm (short version) 56:55 Verida Network x zkPass AMA - Bringing privacy-preserving data verification to web3 1:00:31 2023 Cabot Executive Lecture: William Stoehr ('70), National Geographic Maps | UW-Stout 01:02 Whole-body teleoperation of the Talos robot 50:24 E383 Living Out of Overflow 19:25 Learning English Podcast Conversation | Episode 39 09:26 Elon Musk’s Neuralink is Scarier Than We Think Similar videos 1:24:39 Stanford CS330:Multi-task and Meta Learning | 2020 | Lecture 10 - Model-Based Reinforcement Learning 1:22:26 Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 8 - Model-Based Reinforcement Learning 01:40 Using Parameterized Black Box Priors to Scale Up Model Based Policy Search for Robotics 02:35 18MC009 Search for Targets in a Risky Environment using Multi Objective Optimisation 30:32 Decision Making with Causal Models 1:16:20 Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 11 51:34 Reinforcement Learning 1: Foundations 08:21 Model-Based Policy Optimization (ICML Workshops) 1:21:44 Stanford CS330: Deep Multi-task and Meta Learning | 2020 | Lecture 9 - Meta Reinforcement Learning 1:29:13 Safe and Efficient Exploration in Reinforcement Learning by Andreas Krause 1:17:39 Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 13 1:17:29 Stanford CS330:Multi-task and Meta Learning | 2020 | Lecture 11:Meta RL: Adaptable Models & Policies 51:03 Reinforcement Learning Pretraining for Reinforcement Learning Finetuning 1:04:49 Control Meets Learning Seminar by Andreas Krause (ETH) || June 2, 2021 1:07:40 Efficient Learning from Diverse Sources of Information More results