This Team won the Minecraft RL BASALT Challenge ( Paper Explanation Interview with the authors)
, minerl, minecraft, deeplearning The MineRL BASALT challenge has no reward functions or technical descriptions of what s to be achieved. Instead, the goal of each task is given as a short natural language string, and the agent is evaluated by a team of human judges who rate both how well the goal has been fulfilled, as well as how humanlike the agent behaved. In this video, I interview KAIROS, the winning team of the 2021 challenge, and discuss how they used a combination of machine learning, efficient data collection, hand engineering, and a bit of knowledge about Minecraft to beat all other teams. OUTLINE: 0:00 Introduction 4:10 Paper Overview 11:15 Start of Interview 17:05 First Approach 20:30 State Machine 26:45 Efficient Label Collection 30:00 Navigation Policy 38:15 Odometry Estimation 46:00 Pain Points Learnings 50:40 Live Run Commentary 58:50 What other tasks can be solved 1:01:55 What made the difference 1:07:30 Recommendations Conclusion 1:11:10 Full Runs: Waterf
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