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Igor Mordatch
Researcher at Google
Publications - 114
Citations - 10377
Igor Mordatch is an academic researcher from Google. The author has contributed to research in topics: Reinforcement learning & Computer science. The author has an hindex of 36, co-authored 89 publications receiving 6604 citations. Previous affiliations of Igor Mordatch include OpenAI & University of California, Berkeley.
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Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
TL;DR: An adaptation of actor-critic methods that considers action policies of other agents and is able to successfully learn policies that require complex multi-agent coordination is presented.
Proceedings Article
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
TL;DR: In this article, an actor-critic method was used to learn multi-agent coordination policies in cooperative and competitive multi-player RL games, where agent populations are able to discover various physical and informational coordination strategies.
Posted Content
Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen,Kevin Lu,Aravind Rajeswaran,Kimin Lee,Aditya Grover,Michael Laskin,Pieter Abbeel,Aravind Srinivas,Igor Mordatch +8 more
TL;DR: Despite its simplicity, Decision Transformer matches or exceeds the performance of state-of-the-art model-free offline RL baselines on Atari, OpenAI Gym, and Key-to-Door tasks.
Journal ArticleDOI
Discovery of complex behaviors through contact-invariant optimization
TL;DR: A motion synthesis framework capable of producing a wide variety of important human behaviors that have rarely been studied, including getting up from the ground, crawling, climbing, moving heavy objects, acrobatics, and various cooperative actions involving two characters and their manipulation of the environment is presented.
Posted Content
Emergent Tool Use From Multi-Agent Autocurricula.
TL;DR: This work finds clear evidence of six emergent phases in agent strategy in the authors' environment, each of which creates a new pressure for the opposing team to adapt, and compares hide-and-seek agents to both intrinsic motivation and random initialization baselines in a suite of domain-specific intelligence tests.