A Reminder of its Brittleness: Language Reward Shaping May Hinder Learning for Instruction Following Agents

Author:

Sukai Huang, Nir Lipovetzky, Trevor Cohn

Keyword:

Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Systems and Control (eess.SY)

journal:

date:

2023-05-25 16:00:00

Abstract

Teaching agents to follow complex written instructions has been an important yet elusive goal. One technique for enhancing learning efficiency is language reward shaping (LRS). Within a reinforcement learning (RL) framework, LRS involves training a reward function that rewards behaviours precisely aligned with given language instructions. We argue that the apparent success of LRS is brittle, and prior positive findings can be attributed to weak RL baselines. Specifically, we identified suboptimal LRS designs that reward partially matched trajectories, and we characterised a novel reward perturbation to capture this issue using the concept of loosening task constraints. We provided theoretical and empirical evidence that agents trained using LRS rewards converge more slowly compared to pure RL agents. Our work highlights the brittleness of existing LRS methods, which has been overlooked in the previous studies.

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