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Found 9 papers, 3 papers with code
Robotic Table Tennis: A Case Study into a High Speed Learning System
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets.
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search Spaces
In this paper, we approach the problem of optimizing blackbox functions over large hybrid search spaces consisting of both combinatorial and continuous parameters.
Robotic Table Tennis with Model-Free Reinforcement Learning
We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz.
Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning
Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world.
ES-MAML: Simple Hessian-Free Meta Learning
We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES).
Reinforcement Learning with Chromatic Networks
We present a neural architecture search algorithm to construct compact reinforcement learning (RL) policies, by combining ENAS and ES in a highly scalable and intuitive way.
Reinforcement Learning with Chromatic Networks for Compact Architecture Search
We present a neural architecture search algorithm to construct compact reinforcement learning (RL) policies, by combining ENAS and ES in a highly scalable and intuitive way.
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models: Extension
Finally, we implement an asynchronous version of our algorithm and extend it to the multi-leader setting, where we form groups of workers, each represented by its own local leader (the best performer in a group), and update each worker with a corrective direction comprised of two attractive forces: one to the local, and one to the global leader (the best performer among all workers).
Stochastic Adaptive Quasi-Newton Methods for Minimizing Expected Values
We propose a novel class of stochastic, adaptive methods for minimizing self-concordant functions which can be expressed as an expected value.
