Is feedback all you need? Leveraging natural language feedback in goal-conditioned RL

Despite numerous successes, reinforcement learning is still far from replicating the power and flexibility of behaviour learning in humans. One way to help bridge this gap may be to provide learning agents with richer, more humanlike feedback signals in the form of natural language. We adapt the decision transformer architecture to train agents on the BabyAI environment suite using two different types of generated language feedback, and compare the effect of using language feedback in place of return-to-go and goal description conditioning.