OpenAI Pieter Abbeel — accepted AI-conference papers

2017

Hindsight Experience Replay

Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder +4

NeurIPS 2017poster

Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL). We present a novel technique called Hindsight Experience Replay which allows sample-efficient learning from rewards which are sparse and binary and therefore avoid the need for complicated reward engineering…

Cited by 3290 Source ↗PDF ↗Scholar ↗

2017

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

Ryan Lowe, YI WU, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, Igor Mordatch

NeurIPS 2017poster

We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challenged by an inherent non-stationarity of the environment, while policy gradient suffers from a variance that increases as…

Cited by 6315 Source ↗PDF ↗code ↗Scholar ↗