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Towards robust and domain agnostic reinforcement learning competitions [article]

William Hebgen Guss, Stephanie Milani, Nicholay Topin, Brandon Houghton, Sharada Mohanty, Andrew Melnik, Augustin Harter, Benoit Buschmaas, Bjarne Jaster, Christoph Berganski, Dennis Heitkamp, Marko Henning (+17 others)
2021 arXiv   pre-print
To demonstrate the efficacy of this design, we proposed, organized, and ran the MineRL 2020 Competition on Sample-Efficient Reinforcement Learning.  ...  Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field.  ...  We especially thank Shivam Khandelwal for his help in developing the competition starter-kit and providing constant assistance to the organizers and the participants during the competition.  ... 
arXiv:2106.03748v1 fatcat:6y6am5deljdytd3ng6as2qq4cq

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

William H. Guss, Mario Ynocente Castro, Sam Devlin, Brandon Houghton, Noboru Sean Kuno, Crissman Loomis, Stephanie Milani, Sharada Mohanty, Keisuke Nakata, Ruslan Salakhutdinov, John Schulman, Shinya Shiroshita (+3 others)
2021 arXiv   pre-print
Further we aim to prompt domain agnostic submissions by implementing several novel competition mechanics including action-space randomization and desemantization of observations and actions.  ...  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  ...  To maximize the development of domain-agnostic techniques that enable the application of deep reinforcement learning to sample-limited, real-world domains, such as robotics, we carefully developed a novel  ... 
arXiv:2101.11071v1 fatcat:gzd6vohfavaypnz2vqey6tgkqa

Reinforcement Learning in Practice: Opportunities and Challenges [article]

Yuxi Li
2022 arXiv   pre-print
In this article, we first give a brief introduction to reinforcement learning (RL), and its relationship with deep learning, machine learning and AI.  ...  This article is a gentle discussion about the field of reinforcement learning in practice, about opportunities and challenges, touching a broad range of topics, with perspectives and without technical  ...  risk-averse and uncertainty-aware RL, constrained MDPs and RL, and robust MDPs and RL), d) safe model-based RL, e) safely learning uncertain dynamics (learning adaptive control, learning robust control  ... 
arXiv:2202.11296v2 fatcat:xdtsmme22rfpfn6rgfotcspnhy

Towards Knowledge-capable AI: Agents that See, Speak, Act and Know

Kenneth Marino
Since then, many fields, such as computer vision and natural language processing, have been dominated by large-scale end-to-end learning using large datasets.  ...  However, as our performance on marquee challenges and datasets such as the ImageNet Challenge [294] saturates and the field becomes more concerned with problems such as large-category recognition and problems  ...  Abhinav has been a dogged advocate during my career, providing advice, support and guidance throught my PhD.  ... 
doi:10.1184/r1/19552225.v1 fatcat:rxhy64wyqfb4rjnizil6hrffge