Search Results for author: Dhruva TB

Found 5 papers, 3 papers with code

Distributed Distributional Deterministic Policy Gradients

3 code implementations ICLR 2018 Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy Lillicrap

This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting.

Continuous Control

Probing Physics Knowledge Using Tools from Developmental Psychology

no code implementations3 Apr 2018 Luis Piloto, Ari Weinstein, Dhruva TB, Arun Ahuja, Mehdi Mirza, Greg Wayne, David Amos, Chia-Chun Hung, Matt Botvinick

While some work on this problem has taken the approach of building in components such as ready-made physics engines, other research aims to extract general physical concepts directly from sensory data.

Learning human behaviors from motion capture by adversarial imitation

1 code implementation7 Jul 2017 Josh Merel, Yuval Tassa, Dhruva TB, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, Nicolas Heess

Rapid progress in deep reinforcement learning has made it increasingly feasible to train controllers for high-dimensional humanoid bodies.

Imitation Learning Motion Capture

Emergence of Locomotion Behaviours in Rich Environments

5 code implementations7 Jul 2017 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

The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals.

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