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. It is hard to imagine teaching a child any complex task or skill without, at some point, relying on language to communicate. Written language has also given humans the ability to store information and insights about the world and pass it across generations and continents. Yet, current state-of-the-art reinforcement learning agents are unable to use or understand human language.
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 emerging 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 textual information relating to the task, especially when the language is natural and unstructured
- novel environments enabling such research and approaching complexity of real-world problem settings
The aim of the first workshop on Language in Reinforcement Learning is to steer discussion and research of these problems by bringing together researchers from several communities, including reinforcement learning, robotics, NLP, computer vision and developmental psychology. Through this workshop, we look to identify the main challenges, exchange ideas among and lessons learned from the different research threads, as well as establish requirements for evaluation benchmarks for approaches that integrate language with sequential decision making.