Title: EReLELA: Exploration in Reinforcement Learning via Emergent Language Abstractions
Primary area: reinforcement learning
Keywords: Emergent Communication, Exploration, Reinforcement Learning, Abstraction, Emergent Languages, Natural Languages

Abstract: The ability of AI agents to follow natural language (NL) instructions is important for Human-AI collaboration. Training Embodied AI agents for instruction-following can be done with Reinforcement Learning (RL), yet it poses many challenges. Among which is the exploitation versus exploration trade-off in RL. Previous works have shown that NL-based state abstractions can help address this challenge. However, NLs descriptions have limitations in that they are not always readily available and are expensive to collect. In order to address these limitations, we propose to use the Emergent Communication paradigm, where artificial agents learn an emergent language (EL) in an unsupervised fashion, via referential games. Thus, ELs constitute cheap and readily-available abstractions. In this paper, we investigate (i) how EL-based state abstractions compare to NL-based ones for RL in hard-exploration, procedurally-generated environments, and (ii) how properties of the referential games used to learn ELs impact the quality of the RL exploration and learning. We provide insights about the kind of state abstractions performed by NLs and ELs over RL state spaces, using our proposed Compactness Ambiguity Metric. Our results indicate that our proposed EL-guided agent, entitled EReLELA, achieves similar performance as its NL-based counterparts without its limitations, and is competitive with state-of-the-art approaches in hard-exploration RL. Our work shows that RL agents can leverage unsupervised EL abstractions to greatly improve their exploration skills in sparse reward settings, thus opening new research avenues between Embodied AI and Emergent Communication.

TL;DR: We propose to use state abstractions from cheap Emergent Languages instead of expensive Natural ones to improve Hard-exploration in RL, showing competitive results compared to state-of-the-art.