홈 › Papers

MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned

2022-02-17 · Anssi Kanervisto, Stephanie Milani, Karolis Ramanauskas, Nicholay Topin, Zichuan Lin, Junyou Li, Jianing Shi, Deheng Ye, Qiang Fu, Wei Yang, Weijun Hong, Zhongyue Huang, Haicheng Chen, Guangjun Zeng, Yue Lin, Vincent Micheli, Eloi Alonso, François Fleuret, Alexander Nikulin, Yury Belousov, Oleg Svidchenko, Aleksei Shpilman

Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem. To promote the development of more broadly applicable methods, organizers need to enforce the use of general techniques, the use of sample-efficient methods, and the reproducibility of the results. While beneficial for the research community, these restrictions come at a cost -- increased difficulty. If the barrier for entry is too high, many potential participants are demoralized. With this in mind, we hosted the third edition of the MineRL ObtainDiamond competition, MineRL Diamond 2021, with a separate track in which we permitted any solution to promote the participation of newcomers. With this track and more extensive tutorials and support, we saw an increased number of submissions. The participants of this easier track were able to obtain a diamond, and the participants of the harder track progressed the generalizable solutions in the same task.

📄 PDF Abstract BibTeX arXiv:2202.10583

Code (0)

등록된 구현이 없습니다.

SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition

2021-11-17 · Hangyu Mao, Chao Wang, Xiaotian Hao, Yihuan Mao 외

The MineRL competition is designed for the development of reinforcement learning and imitation learning algorithms that can efficiently leverage human demonstrations to drastically reduce the number of environment intera…

Imitation Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Retrospective on the 2021 BASALT Competition on Learning from Human Feedback

2022-04-14 · Rohin Shah, Steven H. Wang, Cody Wild, Stephanie Milani 외

We held the first-ever MineRL Benchmark for Agents that Solve Almost-Lifelike Tasks (MineRL BASALT) Competition at the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021). The goal of the comp…

Minecraft

The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors

2019-04-22 · William H. Guss, Cayden Codel, Katja Hofmann, Brandon Houghton 외

Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-of-the-art reinforcement learning (RL) systems require a…

Decision Making Deep Reinforcement Learning Efficient Exploration Minecraft+4

Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition

2023-03-23 · Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas, Sander Schulhoff 외

To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 2022. The BASALT challenge asks teams to c…

Minecraft

The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors

2021-01-26 · William H. Guss, Mario Ynocente Castro, Sam Devlin, Brandon Houghton 외

Although deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples, affording only a shrinking segment of the AI community access t…

Decision Making Deep Reinforcement Learning Efficient Exploration Minecraft+3