A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete Trajectories

• URL: http://arxiv.org/abs/2311.01329v1
• Date: Thu, 2 Nov 2023 15:41:09 GMT
• Title: A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete Trajectories
• Authors: Kai Yan, Alexander G. Schwing, Yu-Xiong Wang
• Abstract summary: offline imitation is useful in real-world scenarios where arbitrary interactions are costly and expert actions are unavailable. We propose Trajectory-Aware Learning from Observations (TAILO) to solve MDPs where only task-specific expert states and task-agnostic non-expert state-action pairs are available.
• Score: 122.11358440078581
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Offline imitation from observations aims to solve MDPs where only task-specific expert states and task-agnostic non-expert state-action pairs are available. Offline imitation is useful in real-world scenarios where arbitrary interactions are costly and expert actions are unavailable. The state-of-the-art "DIstribution Correction Estimation" (DICE) methods minimize divergence of state occupancy between expert and learner policies and retrieve a policy with weighted behavior cloning; however, their results are unstable when learning from incomplete trajectories, due to a non-robust optimization in the dual domain. To address the issue, in this paper, we propose Trajectory-Aware Imitation Learning from Observations (TAILO). TAILO uses a discounted sum along the future trajectory as the weight for weighted behavior cloning. The terms for the sum are scaled by the output of a discriminator, which aims to identify expert states. Despite simplicity, TAILO works well if there exist trajectories or segments of expert behavior in the task-agnostic data, a common assumption in prior work. In experiments across multiple testbeds, we find TAILO to be more robust and effective, particularly with incomplete trajectories.

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