Vashisht Madhavan
Vashisht (Vash) is a recent graduate of UC Berkeley, where he received his BS and MS in Computer Science, with a focus in Computer Vision and Artificial Intelligence. At Berkeley, his work focused on perception systems for autonomous vehicles. His interests lie at the intersection of computer vision, machine learning, and reinforcement learning.
Engineering Blog Articles
Creating a Zoo of Atari-Playing Agents to Catalyze the Understanding of Deep Reinforcement Learning
Uber AI Labs releases Atari Model Zoo, an open source repository of both trained Atari Learning Environment agents and tools to better understand them.
Research Papers
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents
F. Such, V. Madhavan, R. Liu, R. Wang, P. Castro, Y. Li, L. Schubert, M. Bellemare, J. Clune, J. Lehman
Much human and computational effort has aimed to improve how deep reinforcement learning algorithms perform on benchmarks such as the Atari Learning Environment. Comparatively less effort has focused on understanding what has been learned by such methods, and investigating and comparing the representations learned by different families of reinforcement learning (RL) algorithms. [...] [PDF at arXiv]
2019
Much human and computational effort has aimed to improve how deep reinforcement learning algorithms perform on benchmarks such as the Atari Learning Environment. Comparatively less effort has focused on understanding what has been learned by such methods, and investigating and comparing the representations learned by different families of reinforcement learning (RL) algorithms. [...] [PDF at arXiv]
2019
Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking...
E. Conti, V. Madhavan, F. Such, J. Lehman, K. Stanley, J. Clune
Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challenging deep reinforcement learning (RL) problems, but are much faster (e.g. hours vs. days) because they parallelize better. [...] [PDF at arXiv]
ViGIL @ NeurIPS 2017 (NeurIPS), 2017
Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challenging deep reinforcement learning (RL) problems, but are much faster (e.g. hours vs. days) because they parallelize better. [...] [PDF at arXiv]
ViGIL @ NeurIPS 2017 (NeurIPS), 2017
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for...
F. Such, V. Madhavan, E. Conti, J. Lehman, K. Stanley, J. Clune
Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. Evolution strategies (ES) can rival backprop-based algorithms such as Q-learning and policy gradients on challenging deep reinforcement learning (RL) problems. [...] [PDF at arXiv]
Deep RL @ NeurIPS 2018, 2017
Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. Evolution strategies (ES) can rival backprop-based algorithms such as Q-learning and policy gradients on challenging deep reinforcement learning (RL) problems. [...] [PDF at arXiv]
Deep RL @ NeurIPS 2018, 2017
