Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction

• URL: http://arxiv.org/abs/2301.10034v3
• Date: Thu, 12 Oct 2023 12:59:56 GMT
• Title: Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction
• Authors: Shaofei Cai, Zihao Wang, Xiaojian Ma, Anji Liu, Yitao Liang
• Abstract summary: We study the problem of learning goal-conditioned policies in Minecraft, a popular, widely accessible yet challenging open-ended environment. We first identify two main challenges of learning such policies: 1) the indistinguishability of tasks from the state distribution, due to the vast scene diversity, and 2) the non-stationary nature of environment dynamics caused by partial observability. We propose Goal-Sensitive Backbone (GSB) for the policy to encourage the emergence of goal-relevant visual state representations.
• Score: 29.32859058651654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of learning goal-conditioned policies in Minecraft, a popular, widely accessible yet challenging open-ended environment for developing human-level multi-task agents. We first identify two main challenges of learning such policies: 1) the indistinguishability of tasks from the state distribution, due to the vast scene diversity, and 2) the non-stationary nature of environment dynamics caused by partial observability. To tackle the first challenge, we propose Goal-Sensitive Backbone (GSB) for the policy to encourage the emergence of goal-relevant visual state representations. To tackle the second challenge, the policy is further fueled by an adaptive horizon prediction module that helps alleviate the learning uncertainty brought by the non-stationary dynamics. Experiments on 20 Minecraft tasks show that our method significantly outperforms the best baseline so far; in many of them, we double the performance. Our ablation and exploratory studies then explain how our approach beat the counterparts and also unveil the surprising bonus of zero-shot generalization to new scenes (biomes). We hope our agent could help shed some light on learning goal-conditioned, multi-task agents in challenging, open-ended environments like Minecraft.

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