1k citations · 4.2k across the 34 of their papers we have counts for
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Interpretable and Pedagogical Examples
Smitha Milli, Pieter Abbeel, Igor Mordatch
Teachers intentionally pick the most informative examples to show their students. However, if the teacher and student are neural networks, the examples that the teacher network lea…
Emergent Complexity via Multi-Agent Competition
Trapit Bansal, Jakub Pachocki, Szymon Sidor +2
Reinforcement learning algorithms can train agents that solve problems in complex, interesting environments. Normally, the complexity of the trained agent is closely related to the…
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
Maruan Al-Shedivat, Trapit Bansal, Yuri Burda +3
Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence. In this paper, we…
Learning with Opponent-Learning Awareness
Jakob N. Foerster, Richard Y. Chen, Maruan Al-Shedivat +3
Multi-agent settings are quickly gathering importance in machine learning. This includes a plethora of recent work on deep multi-agent reinforcement learning, but also can be exten…
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Ryan Lowe, Yi Wu, Aviv Tamar +3
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 challe…
Emergence of Grounded Compositional Language in Multi-Agent Populations
Igor Mordatch, Pieter Abbeel
By capturing statistical patterns in large corpora, machine learning has enabled significant advances in natural language processing, including in machine translation, question ans…