O
Oleg Klimov
Researcher at OpenAI
Publications - 9
Citations - 10772
Oleg Klimov is an academic researcher from OpenAI. The author has contributed to research in topics: Reinforcement learning & Benchmark (computing). The author has an hindex of 7, co-authored 9 publications receiving 6522 citations.
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Proximal Policy Optimization Algorithms
TL;DR: A new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent, are proposed.
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Exploration by Random Network Distillation.
TL;DR: In this article, the authors introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed, where the bonus is the error of a neural network predicting features of the observations given by a fixed randomly initialized neural network.
Proceedings Article
Quantifying Generalization in Reinforcement Learning
TL;DR: It is shown that deeper convolutional architectures improve generalization, as do methods traditionally found in supervised learning, including L2 regularization, dropout, data augmentation and batch normalization.
Proceedings Article
Exploration by random network distillation
TL;DR: An exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed and a method to flexibly combine intrinsic and extrinsic rewards that enables significant progress on several hard exploration Atari games is introduced.
Posted Content
Gotta Learn Fast: A New Benchmark for Generalization in RL
TL;DR: A new reinforcement learning benchmark based on the Sonic the Hedgehog video game franchise is presented, intended to measure the performance of transfer learning and few-shot learning algorithms in the RL domain.