1.6 KiB
1.6 KiB
Documentation
Welcome to the Brittle Star project documentation. This codebase contains the implementations and research for the scientific evaluation of controller modularity in brittle-star-like robots trained using Reinforcement Learning.
For the core codebase, scripts, and contribution history, visit our GitHub Repository.
Design & architecture (/design)
If you are interested in the "why did you do it like this?"
- Actor/critic architecture: Description of the actor-critic pipeline.
- Communication: Message propagation, Nerve-Net style.
- Controllers: Macroscopig brain toplogy, centralized, arm-level, segment-level.
- Input/output: Description of the model's input and output.
- Learning algorithm: RL techniques, i.e. PPO.
- Reward function: Goals, fitness tracking, and reward structures.
API reference (/api)
If you are interested in the "how do I use it?"
- Training: How to configure and run experiments.
- Tracking & Monitoring: Setting up WandB and TensorBoard to monitor runs.
- Simulation: Visualizing and evaluating models.
- Environment: MuJoCo environment interaction and configuration.
- Analysis: Comparing checkpoints and generating plots.
- Evaluation: Evaluating checkpoints and comparing fault tolerance.