Evolved Policy Gradients

3 code implementations • NeurIPS 2018 • Rein Houthooft, Richard Y. Chen, Phillip Isola, Bradly C. Stadie, Filip Wolski, Jonathan Ho, Pieter Abbeel

We propose a metalearning approach for learning gradient-based reinforcement learning (RL) algorithms.

Reinforcement Learning (RL)

Convolutional neural networks that teach microscopes how to image

no code implementations • 21 Sep 2017 • Roarke Horstmeyer, Richard Y. Chen, Barbara Kappes, Benjamin Judkewitz

Deep learning algorithms offer a powerful means to automatically analyze the content of medical images.

Learning with Opponent-Learning Awareness

6 code implementations • 13 Sep 2017 • Jakob N. Foerster, Richard Y. Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, Igor Mordatch

We also show that the LOLA update rule can be efficiently calculated using an extension of the policy gradient estimator, making the method suitable for model-free RL.

Multi-agent Reinforcement Learning

Parameter Space Noise for Exploration

10 code implementations • ICLR 2018 • Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Y. Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, Marcin Andrychowicz

Combining parameter noise with traditional RL methods allows to combine the best of both worlds.

Continuous Control reinforcement-learning +1

UCB Exploration via Q-Ensembles

no code implementations • ICLR 2018 • Richard Y. Chen, Szymon Sidor, Pieter Abbeel, John Schulman

We show how an ensemble of $Q^*$-functions can be leveraged for more effective exploration in deep reinforcement learning.

Q-Learning reinforcement-learning +1