An extension of the Planner-Actor-Reporter framework applied to autonomous vehicles in Highway-Env and CARLA.
If you only want to use Highway-env run the pip install commands:
pip install highway-env
pip install stable-baselines3[extra]
pip install openai
For CARLA, please follow the Nautilus GUI setup guide.
The planner is designed to take in prompts from the user and the environment inference from the reporter and produce an appropriate action for the actor, which is the reinforcement learning agent. We use pre-trained large language models as the planner. In this case, we have used GPT-3.5-turbo as our planner, utilizing the OpenAI's API. We have modeled the API output to only give the appropriate safe action for our vehicle.
We leverage OpenAI's API to use ChatGPT-3.5-Turbo. Below is a demo showcasing ChatGPT's planning skills when presented with a driving scenario from Highway-env.
The Actor component of this framework adaptation serves two purposes: (i) give fine-control commands to the ego vehicle; and (ii) send images and actions to the reporter. The Actor for both Highway-Env and CARLA are trained using DRL algorithms. In future work, we hope to apply multi-goal reinforcement learning so that the Planner can issue more high-level commands for the Actor.
For Highway-Env we train a DQN model that accepts commands from the Planner. We made custom Highway-Envs that challenged ChatGPT's reasoning skills.
In CARLA we present a simple DRL pipeline. Stacked gray-scaled images are passed into a CNN, concatenated with the ego vehicle's throttle, steering, and previous steering command. We train PPO and SAC.
The role of the reporter is to transform the Actor's observations and actions into a representation for the planner - in this case, text.
The hard-coded reporter is a trivial ad-lib style report generator. It parses Highway-Env states for relevant information such as the ego vehicle's pose, speed, and other agent's positions. This information is formatted into a legible reporter for the Planner to decide the next move.
See an example here
Text-based state spaces are not always available. CARLA, for instance, offers RGB cameras, LiDAR, and semantic segmentation sensors. This motivated us to fine-tune a VQA model so that we can pass in an image and generate a report of the driving scene. We LLaVA for this implementation.
See the data collector page for more info on getting training data to fine-tune LLaVA.
As for compute, we got by with two GeFORCE RTX-3090 in the Nautilus cluster. We would recommend a GPU with more video memory.
More instructions coming soon! For now, checkout our sample scripts that combines all components here
For Highway-Env we compare ChatGPT's reasoning abilities against another framework that uses ChatGPT as an embodied reasoner.
In CARLA, we demo the entire framework in action. The ego vehicle's task is to follow a car lane and avoid collision; there is a still vehicle obstacle. The Reporter gets a pair of images from the ego vehicle. The generated report is sent to the Planner which decides to pump to brakes to prevent an accident.
