Y
Yuval Tassa
Researcher at Google
Publications - 58
Citations - 16371
Yuval Tassa is an academic researcher from Google. The author has contributed to research in topics: Reinforcement learning & Humanoid robot. The author has an hindex of 31, co-authored 53 publications receiving 13493 citations. Previous affiliations of Yuval Tassa include University of Washington & Hebrew University of Jerusalem.
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Continuous control with deep reinforcement learning
Timothy P. Lillicrap,Jonathan J. Hunt,Alexander Pritzel,Nicolas Heess,Tom Erez,Yuval Tassa,David Silver,Daan Wierstra +7 more
TL;DR: This work presents an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces, and demonstrates that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.
Proceedings ArticleDOI
MuJoCo: A physics engine for model-based control
TL;DR: A new physics engine tailored to model-based control, based on the modern velocity-stepping approach which avoids the difficulties with spring-dampers, which can compute both forward and inverse dynamics.
Proceedings Article
Continuous control with deep reinforcement learning
Timothy P. Lillicrap,Jonathan J. Hunt,Alexander Pritzel,Nicolas Heess,Tom Erez,Yuval Tassa,David Silver,Daan Wierstra +7 more
TL;DR: In this paper, an actor-critic, model-free algorithm based on the deterministic policy gradient is proposed to operate over continuous action spaces, which is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain.
Proceedings ArticleDOI
Synthesis and stabilization of complex behaviors through online trajectory optimization
TL;DR: An online trajectory optimization method and software platform applicable to complex humanoid robots performing challenging tasks such as getting up from an arbitrary pose on the ground and recovering from large disturbances using dexterous acrobatic maneuvers is presented.
Posted Content
Emergence of Locomotion Behaviours in Rich Environments
Nicolas Heess,Dhruva Tb,Srinivasan Sriram,Jay Lemmon,Josh Merel,Greg Wayne,Yuval Tassa,Tom Erez,Ziyu Wang,S. M. Ali Eslami,Martin Riedmiller,David Silver +11 more
TL;DR: This paper explores how a rich environment can help to promote the learning of complex behavior, and finds that this encourages the emergence of robust behaviours that perform well across a suite of tasks.