This repository implements the Cross Entropy Method (CEM) for control in pytorch.

Clone repository somewhere, then pip3 install -e . to install in editable mode. See tests/pendulum_approximate.py for usage with a neural network approximating the pendulum dynamics. Basic use case is shown below

from pytorch_cem import cem
# create controller with chosen parameters
ctrl = cem.CEM(dynamics, running_cost,  nx, nu, num_samples=N_SAMPLES, num_iterations=SAMPLE_ITER,
                      horizon=TIMESTEPS, device=d, num_elite=N_ELITES,
                      u_max=torch.tensor(ACTION_HIGH, dtype=torch.double, device=d), init_cov_diag=1)

# 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
• cost <- running_cost(state, action) function
  • cost is K x 1, state is K x nx, action is K x nu

• Parallel/batch pytorch implementation for accelerated sampling
• Control bounds

You'll need to install gym to run the tests (for the pendulum environment).

Under tests you can find the CEM method applied to known pendulum dynamics and approximate pendulum dynamics (with a 2 layer feedforward net estimating the state residual). Using a continuous angle representation (feeding cos(\theta), sin(\theta) instead of \theta directly) makes a huge difference. Although both works, the continuous representation is much more robust to controller parameters and random seed. In addition, the problem of continuing to spin after over-swinging does not appear.

• pytorch MPPI - an alternative MPC method with similar API as this project (faster and works better in general than CEM)