LaReL: Language and Reinforcement Learning

Laetitia Teodorescu · Laura Ruis · Tristan Karch · Cédric Colas · Paul Barde · Jelena Luketina · Athul Jacob · Pratyusha Sharma · Edward Grefenstette · Jacob Andreas · Marc-Alexandre Côté

Room 391
Abstract Workshop Website
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Fri 2 Dec, 6:30 a.m. PST


Language is one of the most impressive human accomplishments and is believed to be the core to our ability to learn, teach, reason and interact with others. Learning many complex tasks or skills would be significantly more challenging without relying on language to communicate, and language is believed to have a structuring impact on human thought. Written language has also given humans the ability to store information and insights about the world and pass it across generations and continents. Yet, the ability of current state-of-the art reinforcement learning agents to understand natural language is limited.

Practically speaking, the ability to integrate and learn from language, in addition to rewards and demonstrations, has the potential to improve the generalization, scope and sample efficiency of agents. For example, agents that are capable of transferring domain knowledge from textual corpora might be able to much more efficiently explore in a given environment or to perform zero or few shot learning in novel environments. Furthermore, many real-world tasks, including personal assistants and general household robots, require agents to process language by design, whether to enable interaction with humans, or simply use existing interfaces.

To support this field of research, we are interested in fostering the discussion around:

- Methods that can effectively link language to actions and observations in the environment;
- Research into language roles beyond encoding goal states, such as structuring hierarchical policies,
- Communicating domain knowledge or reward shaping;
- Methods that can help identify and incorporate outside textual information about the task, or general-purpose semantics learned from outside corpora;
- Novel environments and benchmarks enabling such research and approaching complexity of real-world problem settings.

The aim of the workshop on Language in Reinforcement Learning (LaReL) is to steer discussion and research of these problems by bringing together researchers from several communities, including reinforcement learning, robotics, natural language processing, computer vision and cognitive psychology.

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