42 citations · 80 across the 6 of their papers we have counts for
6 papers
Infinite Photorealistic Worlds using Procedural Generation
Alexander Raistrick, Lahav Lipson, Zeyu Ma +12
We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world. Infinigen is entirely procedural: every asset, from shape to texture, is generated…
Learning Domain Invariant Representations in Goal-conditioned Block MDPs
Beining Han, Chongyi Zheng, Harris Chan +3
Deep Reinforcement Learning (RL) is successful in solving many complex Markov Decision Processes (MDPs) problems. However, agents often face unanticipated environmental changes aft…
On the Estimation Bias in Double Q-Learning
Zhizhou Ren, Guangxiang Zhu, Hao Hu +3
Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operation. Its variants in the deep Q-le…
Off-Policy Reinforcement Learning with Delayed Rewards
Beining Han, Zhizhou Ren, Zuofan Wu +2
We study deep reinforcement learning (RL) algorithms with delayed rewards. In many real-world tasks, instant rewards are often not readily accessible or even defined immediately af…
Off-Policy Multi-Agent Decomposed Policy Gradients
Yihan Wang, Beining Han, Tonghan Wang +2
Multi-agent policy gradient (MAPG) methods recently witness vigorous progress. However, there is a significant performance discrepancy between MAPG methods and state-of-the-art mul…
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization
Jianhao Wang, Zhizhou Ren, Beining Han +2
Value factorization is a popular and promising approach to scaling up multi-agent reinforcement learning in cooperative settings, which balances the learning scalability and the re…