/HJxB

Continuous-Time/State/Action Fitted Value Iteration via Hamilton-Jacobi-Bellman (HJB)

Primary LanguagePython

HJxB

Continuous Fitted Value Iteration based on closed-form solution of Hamilton-Jacobi-Bellman equation for affine systems, implemented in JAX. This method was first used in this paper and tested on a number of classic contorl problems like cartpole or pendulum swig-up tasks.

This repo contains:

  • Extensibility for custom environment definition
  • Linearization of forward dynamics and reward functions
  • A number of different solvers for bellman backup optimization
  • Various integrators for forward dynamics
  • Various data collection and storage methods
  • Extensive configurability
  • Tensorboard and file logging

Usage

For now:

python main.py --config-file=./config/Pendulum.yaml
tensorboard --logdir=./logs/<log_dir>