Planning Immediate Landmarks of Targets for Model-Free Skill Transfer across Agents

• URL: http://arxiv.org/abs/2212.09033v1
• Date: Sun, 18 Dec 2022 08:03:21 GMT
• Title: Planning Immediate Landmarks of Targets for Model-Free Skill Transfer across Agents
• Authors: Minghuan Liu, Zhengbang Zhu, Menghui Zhu, Yuzheng Zhuang, Weinan Zhang, Jianye Hao
• Abstract summary: We propose PILoT, i.e., Planning Immediate Landmarks of Targets. PILoT learns a goal-conditioned state planner and distills a goal-planner to plan immediate landmarks in a model-free style. We show the power of PILoT on various transferring challenges, including few-shot transferring across action spaces and dynamics.
• Score: 34.56191646231944
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In reinforcement learning applications like robotics, agents usually need to deal with various input/output features when specified with different state/action spaces by their developers or physical restrictions. This indicates unnecessary re-training from scratch and considerable sample inefficiency, especially when agents follow similar solution steps to achieve tasks. In this paper, we aim to transfer similar high-level goal-transition knowledge to alleviate the challenge. Specifically, we propose PILoT, i.e., Planning Immediate Landmarks of Targets. PILoT utilizes the universal decoupled policy optimization to learn a goal-conditioned state planner; then, distills a goal-planner to plan immediate landmarks in a model-free style that can be shared among different agents. In our experiments, we show the power of PILoT on various transferring challenges, including few-shot transferring across action spaces and dynamics, from low-dimensional vector states to image inputs, from simple robot to complicated morphology; and we also illustrate a zero-shot transfer solution from a simple 2D navigation task to the harder Ant-Maze task.

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