This repository implements the improved Cross Entropy Method (iCEM) with approximate dynamics in pytorch, from this paper.

MPPI typically requires actual trajectory samples, but this paper showed that it could be done with approximate dynamics (such as with a neural network) using importance sampling.

Thus it can be used in place of other trajectory optimization methods such as the Cross Entropy Method (CEM), or random shooting.

• pytorch CEM - alternative sampling based MPC
• pytorch MPPI - alternative sampling based MPC
• iCEM - original paper's numpy implementation and experiments code

pip install pytorch-icem

for running tests, install with

pip install pytorch-icem[test]

for development, clone the repository then install in editable mode

pip install -e .

See tests/pendulum_approximate_continuous.py for usage with a neural network approximating the pendulum dynamics. Basic use case is shown below

from pytorch_icem import iCEM

# create controller with chosen parameters
ctrl = icem.iCEM(dynamics, terminal_cost, nx, nu, sigma=sigma,
                 warmup_iters=10, online_iters=10,
                 num_samples=N_SAMPLES, num_elites=10, horizon=TIMESTEPS, device=d, )

# assuming you have a gym-like env
obs = env.reset()
for i in range(100):
    action = ctrl.command(obs)
    obs, reward, done, _, _ = env.step(action.cpu().numpy())

• pytorch (>= 1.0)
• next state <- dynamics(state, action) function (doesn't have to be true dynamics)
  • state is K x nx, action is K x nu
• trajectory cost <- cost(state, action) function for the whole state action trajectory, T is the horizon
  • cost is K x 1, state is K x T x nx, action is K x T x nu

• Parallel/batch pytorch implementation for accelerated sampling