This project sets up navigation experiments in Habitat using Soft Actor-Critic.
The proposed stages are to train a baseline, collect demonstrations from a language model, learn a reward from those demonstrations, and train another policy with that reward. Keeping the baseline and reward-learning stages separate is intended to make their effects easier to compare.
The code is a scaffold for that process. Whether the demonstrations provide useful navigation information remains a question for the experiment.