storm_kit.mpc.control.mppi module¶
-
class
MPPI
(d_action, horizon, init_cov, init_mean, base_action, beta, num_particles, step_size_mean, step_size_cov, alpha, gamma, kappa, n_iters, action_lows, action_highs, null_act_frac=0.0, rollout_fn=None, sample_mode='mean', hotstart=True, squash_fn='clamp', update_cov=False, cov_type='sigma_I', seed=0, sample_params={'filter_coeffs': None, 'fixed_samples': True, 'seed': 0, 'type': 'halton'}, tensor_args={'device': device(type='cpu'), 'dtype': torch.float32}, visual_traj='state_seq')[source]¶ Bases:
storm_kit.mpc.control.olgaussian_mpc.OLGaussianMPC
Class that implements Model Predictive Path Integral Controller
Implementation is based on Williams et. al, Information Theoretic MPC for Model-Based Reinforcement Learning with additional functions for updating the covariance matrix and calculating the soft-value function.
- Parameters
base_action (str) – Action to append at the end when shifting solution to next timestep ‘random’ : appends random action ‘null’ : appends zero action ‘repeat’ : repeats second to last action
num_particles (int) – Number of action sequences sampled at every iteration
-
_abc_impl
= <_abc_data object>¶