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

03/23/2023
by   Stephanie Milani, et al.
0

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 compete to develop algorithms to solve tasks with hard-to-specify reward functions in Minecraft. Through this competition, we aimed to promote the development of algorithms that use human feedback as channels to learn the desired behavior. We describe the competition and provide an overview of the top solutions. We conclude by discussing the impact of the competition and future directions for improvement.

READ FULL TEXT

page 8

page 13

page 14

page 17

research
04/14/2022

Retrospective on the 2021 BASALT Competition on Learning from Human Feedback

We held the first-ever MineRL Benchmark for Agents that Solve Almost-Lif...
research
03/10/2020

Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning

To facilitate research in the direction of sample-efficient reinforcemen...
research
07/05/2021

The MineRL BASALT Competition on Learning from Human Feedback

The last decade has seen a significant increase of interest in deep lear...
research
12/18/2021

Improving Learning-to-Defer Algorithms Through Fine-Tuning

The ubiquity of AI leads to situations where humans and AI work together...
research
04/10/2023

Learning a Universal Human Prior for Dexterous Manipulation from Human Preference

Generating human-like behavior on robots is a great challenge especially...
research
12/07/2021

Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft

Real-world tasks of interest are generally poorly defined by human-reada...

Please sign up or login with your details

Forgot password? Click here to reset