It has been argued that language can be a very powerful way to compress information about the world. In fact, the learning of humans is significantly sped up around the time they start understanding and using language. A natural question, then, is whether the same can be argued for sequential decision-making systems that either learn to optimize a single or multiple, task. To this end, there has been a surge of works exploring the use of Language in Reinforcement Learning (RL) and Meta-RL. The goal of this blog post is to try and explain some of the recent works in this sub-field and help elucidate how language can help with incorporating structure about the environment to improve learning generalization in (Meta) RL.


We live in a time where sequential decision-making techniques like Reinforcement Learning (RL) are making increasingly larger strides, not just in robot manipulation, and games considered once to be the pinnacle of human intelligence, but also in an increasingly novel set of scenarios like chemistry and logistics. While a lot of the theory in RL existed from classical times, the success of integrating Deep Neural Networks (DNNs) as function approximators has created a sort of Cambrian explosion in the last years. Traditionally, a major focus of the field was on developing techniques that can learn to solve an inherent optimization problem, like learning a solution to a maze. As the field evolved in the last years, its scope has started to broaden to encompass bigger questions, like whether a learned policy to solve a maze can generalize to other configurations (Generalization in RL), or whether a policy can be transferred to scenarios where conditions differ slightly from the training conditions (Robustness, Deployability), or how can we design agents in a data-driven manner (AutoRL). Yet, a major bottleneck in current RL techniques is that they are not yet, largely, ready for real-world deployment.

Parallelly, another Cambrian explosion has been happening in the field of Natural Language Processing (NLP). Language models have come a long way since the days of word embedding and sequence-sequence models, to the agent of attention and pre-trained models. Crucially, as this growth continues with newer innovations like ChatGPT, it also leads us to innovative applications of these language models in other fields of Machine Learning, including RL.

In this blog post, I will explore the connection between Natural Language and RL through some recent and not-so-recent works that I find very interesting. Since this topic is vast, so much so that a full survey has been written on it, I will limit the focus to how language can be used to augment RL pipelines (Language-assisted RL), and not on the use of RL for language training (RL for language). Through this blog, my hope it to visit two ideas in using Language for RL that exist at two very different points in the Deep RL timelines, and yet hold significance in the way they use language to augment the RL pipeline.

RL Basics

To better cater to audiences beyond the RL community, I think it would be good to briefly revise some core concepts. RL folks are more than welcome to skip to the next section. I am going to try my best to keep it less math-oriented, but I apologize in advance on behalf of the symbols I will use.