Compositional Instruction Following with Language Models and Reinforcement Learning

• URL: http://arxiv.org/abs/2501.12539v1
• Date: Tue, 21 Jan 2025 23:06:34 GMT
• Title: Compositional Instruction Following with Language Models and Reinforcement Learning
• Authors: Vanya Cohen, Geraud Nangue Tasse, Nakul Gopalan, Steven James, Matthew Gombolay, Ray Mooney, Benjamin Rosman,
• Abstract summary: We introduce a compositionally-enabled reinforcement learning language agent (CERLLA) Our method reduces the complexity sample of tasks specified with language by leveraging compositional policy representations and a semantic approximation. Our model attains a higher success rate and learns in fewer steps than the non-compositional baseline.
• Score: 10.513214582226649
• License:
• Abstract: Combining reinforcement learning with language grounding is challenging as the agent needs to explore the environment while simultaneously learning multiple language-conditioned tasks. To address this, we introduce a novel method: the compositionally-enabled reinforcement learning language agent (CERLLA). Our method reduces the sample complexity of tasks specified with language by leveraging compositional policy representations and a semantic parser trained using reinforcement learning and in-context learning. We evaluate our approach in an environment requiring function approximation and demonstrate compositional generalization to novel tasks. Our method significantly outperforms the previous best non-compositional baseline in terms of sample complexity on 162 tasks designed to test compositional generalization. Our model attains a higher success rate and learns in fewer steps than the non-compositional baseline. It reaches a success rate equal to an oracle policy's upper-bound performance of 92%. With the same number of environment steps, the baseline only reaches a success rate of 80%.

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