Repository for environment encoder.

The idea is to train an RL agent to play games given a multimodal model's embeddings, instead of the actual environment. I believe embeddings are a more universal representation, and if learned on it might make it easier for an agent to generalize to unseen environments.

Run the following to install dependencies

apt-get install swig
conda env create -f conda_env.yml
pip install flash-attn --no-build-isolation

• Setup RL training code
• Setup code to extract VLM embeddings from Gym environment frames.
• Train RL agent on embeddings instead of game frames.
• Improve efficiency of generating VLM embeddings through caching and batching.
• Setup code to autotune hyperparameters for RL agent.
• Train autoencoder on Atari game frame VLM embeddings.
• Replace embeddings input to RL agent with autoencoder dense representation input.
• Train RL agent on autoencoder dense representation.
• Show improved performance / generalizability

Big thanks to CleanRL for providing the basis of the RL training code and hyperparameter tuning code, made my life a lot easier :)