Progressively Efficient Learning

• URL: http://arxiv.org/abs/2310.13004v1
• Date: Fri, 13 Oct 2023 07:52:04 GMT
• Title: Progressively Efficient Learning
• Authors: Ruijie Zheng, Khanh Nguyen, Hal Daum\'e III, Furong Huang, Karthik Narasimhan
• Abstract summary: We develop a novel learning framework named Communication-Efficient Interactive Learning (CEIL) CEIL leads to emergence of a human-like pattern where the learner and the teacher communicate efficiently by exchanging increasingly more abstract intentions. Agents trained with CEIL quickly master new tasks, outperforming non-hierarchical and hierarchical imitation learning by up to 50% and 20% in absolute success rate.
• Score: 58.6490456517954
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Assistant AI agents should be capable of rapidly acquiring novel skills and adapting to new user preferences. Traditional frameworks like imitation learning and reinforcement learning do not facilitate this capability because they support only low-level, inefficient forms of communication. In contrast, humans communicate with progressive efficiency by defining and sharing abstract intentions. Reproducing similar capability in AI agents, we develop a novel learning framework named Communication-Efficient Interactive Learning (CEIL). By equipping a learning agent with an abstract, dynamic language and an intrinsic motivation to learn with minimal communication effort, CEIL leads to emergence of a human-like pattern where the learner and the teacher communicate progressively efficiently by exchanging increasingly more abstract intentions. CEIL demonstrates impressive performance and communication efficiency on a 2D MineCraft domain featuring long-horizon decision-making tasks. Agents trained with CEIL quickly master new tasks, outperforming non-hierarchical and hierarchical imitation learning by up to 50% and 20% in absolute success rate, respectively, given the same number of interactions with the teacher. Especially, the framework performs robustly with teachers modeled after human pragmatic communication behavior.

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