3.7k citations · 6.5k across the 15 of their papers we have counts for
16 papers
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning
Dhruva Tirumala, Markus Wulfmeier, Ben Moran +13
We apply multi-agent deep reinforcement learning (RL) to train end-to-end robot soccer policies with fully onboard computation and sensing via egocentric RGB vision. This setting r…
Replay across Experiments: A Natural Extension of Off-Policy RL
Dhruva Tirumala, Thomas Lampe, Jose Enrique Chen +9
Replaying data is a principal mechanism underlying the stability and data efficiency of off-policy reinforcement learning (RL). We present an effective yet simple framework to exte…
Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning
Tuomas Haarnoja, Ben Moran, Guy Lever +25
We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be compo…
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration
Giulia Vezzani, Dhruva Tirumala, Markus Wulfmeier +13
The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common a…
NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields
Arunkumar Byravan, Jan Humplik, Leonard Hasenclever +8
We present a system for applying sim2real approaches to "in the wild" scenes with realistic visuals, and to policies which rely on active perception using RGB cameras. Given a shor…
Forgetting and Imbalance in Robot Lifelong Learning with Off-policy Data
Wenxuan Zhou, Steven Bohez, Jan Humplik +5
Robots will experience non-stationary environment dynamics throughout their lifetime: the robot dynamics can change due to wear and tear, or its surroundings may change over time.…
Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors
Steven Bohez, Saran Tunyasuvunakool, Philemon Brakel +18
We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots. Our approach builds upon previous work on imitati…
From Motor Control to Team Play in Simulated Humanoid Football
Siqi Liu, Guy Lever, Zhe Wang +19
Intelligent behaviour in the physical world exhibits structure at multiple spatial and temporal scales. Although movements are ultimately executed at the level of instantaneous mus…
Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery
Kristian Hartikainen, Xinyang Geng, Tuomas Haarnoja +1
Reinforcement learning requires manual specification of a reward function to learn a task. While in principle this reward function only needs to specify the task goal, in practice…
Soft Actor-Critic Algorithms and Applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen +8
Model-free deep reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks. However, these methods…
Learning to Walk via Deep Reinforcement Learning
Tuomas Haarnoja, Sehoon Ha, Aurick Zhou +3
Deep reinforcement learning (deep RL) holds the promise of automating the acquisition of complex controllers that can map sensory inputs directly to low-level actions. In the domai…
Latent Space Policies for Hierarchical Reinforcement Learning
Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel +1
We address the problem of learning hierarchical deep neural network policies for reinforcement learning. In contrast to methods that explicitly restrict or cripple lower layers of…
Composable Deep Reinforcement Learning for Robotic Manipulation
Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou +3
Model-free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, mod…
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel +1
Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks. However, these methods typically suffer f…
Reinforcement Learning with Deep Energy-Based Policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel +1
We propose a method for learning expressive energy-based policies for continuous states and actions, which has been feasible only in tabular domains before. We apply our method to…
Backprop KF: Learning Discriminative Deterministic State Estimators
Tuomas Haarnoja, Anurag Ajay, Sergey Levine +1
Generative state estimators based on probabilistic filters and smoothers are one of the most popular classes of state estimators for robots and autonomous vehicles. However, genera…