1k citations · 1.8k across the 17 of their papers we have counts for
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Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimization
Runlong Zhou, Zelin He, Yuandong Tian +2
Over the recent years, reinforcement learning (RL) starts to show promising results in tackling combinatorial optimization (CO) problems, in particular when coupled with curriculum…
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning
Qian Long, Zihan Zhou, Abhibav Gupta +3
In multi-agent games, the complexity of the environment can grow exponentially as the number of agents increases, so it is particularly challenging to learn good policies when the…
Multi-Task Reinforcement Learning with Soft Modularization
Ruihan Yang, Huazhe Xu, Yi Wu +1
Multi-task learning is a very challenging problem in reinforcement learning. While training multiple tasks jointly allow the policies to share parameters across different tasks, th…
Influence-Based Multi-Agent Exploration
Tonghan Wang, Jianhao Wang, Yi Wu +1
Intrinsically motivated reinforcement learning aims to address the exploration challenge for sparse-reward tasks. However, the study of exploration methods in transition-dependent…
Emergent Tool Use From Multi-Agent Autocurricula
Bowen Baker, Ingmar Kanitscheider, Todor Markov +4
Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocu…
Learning and Planning with a Semantic Model
Yi Wu, Yuxin Wu, Aviv Tamar +3
Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this cha…
Building Generalizable Agents with a Realistic and Rich 3D Environment
Yi Wu, Yuxin Wu, Georgia Gkioxari +1
Teaching an agent to navigate in an unseen 3D environment is a challenging task, even in the event of simulated environments. To generalize to unseen environments, an agent needs t…
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…