curobo.opt.particle.parallel_es module

class ParallelESConfig( d_action: 'int', action_lows: 'List[float]', action_highs: 'List[float]', action_horizon: 'int', horizon: 'int', n_iters: 'int', cold_start_n_iters: 'Union[int, None]', rollout_fn: 'RolloutBase', tensor_args: 'TensorDeviceType', use_cuda_graph: 'bool', store_debug: 'bool', debug_info: 'Any', n_problems: 'int', num_particles: 'Union[int, None]', sync_cuda_time: 'bool', use_coo_sparse: 'bool', gamma: float, sample_mode: curobo.opt.particle.particle_opt_base.SampleMode, seed: int, calculate_value: bool, store_rollouts: bool, init_mean: float, init_cov: float, base_action: curobo.opt.particle.parallel_mppi.BaseActionType, step_size_mean: float, step_size_cov: float, null_act_frac: float, squash_fn: curobo.opt.particle.particle_opt_utils.SquashType, cov_type: curobo.opt.particle.parallel_mppi.CovType, sample_params: curobo.util.sample_lib.SampleConfig, update_cov: bool, random_mean: bool, beta: float, alpha: float, kappa: float, sample_per_problem: bool, learning_rate: float = 0.1, )

Bases: ParallelMPPIConfig

learning_rate: float = 0.1

static create_data_dict( data_dict: Dict, rollout_fn: RolloutBase, tensor_args: TensorDeviceType = TensorDeviceType(device=device(type='cuda', index=0), dtype=torch.float32, collision_geometry_dtype=torch.float32, collision_gradient_dtype=torch.float32, collision_distance_dtype=torch.float32), child_dict: Dict | None = None, )

Helper function to create dictionary from optimizer parameters and rollout class.

Parameters:

data_dict – optimizer parameters dictionary.
rollout_fn – rollout function.
tensor_args – tensor cuda device.
child_dict – new dictionary where parameters will be stored.

Returns:

Dictionary with parameters to create a OptimizerConfig

init_mean: float

init_cov: float

base_action: BaseActionType

step_size_mean: float

step_size_cov: float

null_act_frac: float

squash_fn: SquashType

cov_type: CovType

sample_params: SampleConfig

update_cov: bool

random_mean: bool

beta: float

alpha: float

gamma: float

kappa: float

sample_per_problem: bool

sample_mode: SampleMode

seed: int

calculate_value: bool

store_rollouts: bool

d_action: int: Number of optimization variables per timestep.

action_lows: List[float]: Lower bound for optimization variables.

action_highs: List[float]: Higher bound for optimization variables

action_horizon: int

horizon: int: Number of timesteps in optimization state, total variables = d_action * horizon

n_iters: int: Number of iterations to run optimization

cold_start_n_iters: int | None: Number of iterations to run optimization during the first instance. Setting to None will use n_iters. This parameter is useful in MPC like settings where we need to run many iterations during initialization (cold start) and then run only few iterations (warm start).

rollout_fn: RolloutBase: Rollout function to use for computing cost, given optimization variables.

tensor_args: TensorDeviceType: Tensor device to use for optimization.

use_cuda_graph: bool: Capture optimization iteration in a cuda graph and replay graph instead of eager execution. Enabling this can make optimization 10x faster. But changing control flow, tensor shapes, or problem type is not allowed.

store_debug: bool: Record debugging data such as optimization variables, and cost at every iteration. Enabling this will disable cuda graph.

debug_info: Any: Use this to record additional attributes from rollouts.

n_problems: int: Number of parallel problems to optimize.

num_particles: int | None: Number of particles to use per problem. Common optimization solvers use many particles to optimize a single problem. E.g., MPPI rolls out many parallel samples and computes a weighted mean. In cuRobo, Quasi-Newton solvers use particles to run many line search magnitudes. Total optimization batch size = n_problems * num_particles.

sync_cuda_time: bool: Synchronize device before computing solver time.

use_coo_sparse: bool: Matmul with a Sparse tensor is used to create particles for each problem index to save memory and compute. Some versions of pytorch do not support coo sparse, specifically during torch profile runs. Set this to False to use a standard tensor.

class ParallelES( config: ParallelESConfig | None = None, )

Bases: ParallelMPPI, ParallelESConfig

Base optimization solver class

Parameters:: config – Initialized with parameters from a dataclass.

_compute_mean(w, actions)

_exp_util_from_costs(costs)

_exp_util(total_costs)

_compute_mean_covariance( costs, actions, )

_update_distribution( trajectories: Trajectory, )

Update current control distribution using rollout trajectories

Parameters:

trajectories –

dict Rollout trajectories. Contains the following fields observations : torch.tensor

observations along rollouts

actionstorch.tensor: actions sampled from control distribution along rollouts
coststorch.tensor: step costs along rollouts

_calc_val( trajectories: Trajectory, ): Calculate value of state given rollouts from a policy

_call_cuda_opt_iters( init_act: Tensor, )

_compute_covariance( w, actions, )

_compute_total_cost(costs): Calculate weights using exponential utility

_get_action_seq( mode: SampleMode, )

Get action sequence to execute on the system based on current control distribution

Parameters:: mode – {‘mean’, ‘sample’} how to choose action to be executed ‘mean’ plays mean action and ‘sample’ samples from the distribution

_initialize_cuda_graph( init_act: Tensor, shift_steps=0, )

_optimize( init_act: Tensor, shift_steps=0, n_iters=None, )

Optimize for best action at current state

Parameters:

state (torch.Tensor) – state to calculate optimal action from
calc_val (bool) – If true, calculate the optimal value estimate of the state along with action

Returns:

action (torch.Tensor) – next action to execute
value (float) – optimal value estimate (default: 0.)
info (dict) – dictionary with side-information

_run_opt_iters( init_act: Tensor, shift_steps=0, n_iters=None, )

abstract _shift(shift_steps=0)

Shift the variables in the solver to hotstart the next timestep.

Parameters:: shift_steps – Number of timesteps to shift.

_update_cov_scale( new_cov=None, )

_update_problem_kernel( n_problems: int, num_particles: int, )

Update matrix used to map problem index to number of particles.

Parameters:

n_problems – Number of optimization problems.
num_particles – Number of particles per problem.

check_convergence(): Checks if controller has converged Returns False by default

Helper function to create dictionary from optimizer parameters and rollout class.

Parameters:

data_dict – optimizer parameters dictionary.
rollout_fn – rollout function.
tensor_args – tensor cuda device.
child_dict – new dictionary where parameters will be stored.

Returns:

Dictionary with parameters to create a OptimizerConfig

property entropy

property full_cov

property full_inv_cov

property full_scale_tril

generate_noise( shape, base_seed=None, ): Generate correlated noisy samples using autoregressive process

generate_rollouts( init_act=None, )

Samples a batch of actions, rolls out trajectories for each particle and returns the resulting observations, costs, actions

Parameters:: state (dict or np.ndarray) – Initial state to set the simulation problem to

get_all_rollout_instances() → List[RolloutBase]: Get all instances of Rollout class in the optimizer.

get_nproblem_tensor(x): This function takes an input tensor of shape (n_problem,….) and converts it into (n_particles,…).

get_rollouts()

learning_rate: float = 0.1

optimize( opt_tensor: Tensor, shift_steps=0, n_iters=None, ) → Tensor

Find a solution through optimization given the initial values for variables.

Parameters:

opt_tensor – Initial value of optimization variables. Shape: [n_problems, action_horizon, d_action]
shift_steps – Shift variables along action_horizon. Useful in mpc warm-start setting.
n_iters – Override number of iterations to run optimization.

Returns:

Optimized values returned as a tensor of shape [n_problems, action_horizon, d_action].

reset(): Reset the optimizer

reset_covariance()

reset_cuda_graph(): Reset CUDA Graphs. This does not work, workaround is to create a new instance.

reset_distribution(): Reset control distribution

reset_mean()

reset_seed(): Reset seeds.

reset_shape(): Reset any flags in rollout class. Useful to reinitialize tensors for a new shape.

sample_actions(init_act): Sample actions from current control distribution

property squashed_mean

update_init_mean(init_mean)

update_nproblems(n_problems)

Update the number of problems that need to be optimized.

Parameters:: n_problems – number of problems.

update_num_particles_per_problem( num_particles_per_problem, )

update_params( goal: Goal, )

Update parameters in the curobo.rollout.rollout_base.RolloutBase instance.

Parameters:: goal – parameters to update rollout instance.

update_samples()

update_seed(init_act)

d_action: int: Number of optimization variables per timestep.

action_lows: List[float]: Lower bound for optimization variables.

action_highs: List[float]: Higher bound for optimization variables

action_horizon: int

horizon: int: Number of timesteps in optimization state, total variables = d_action * horizon

n_iters: int: Number of iterations to run optimization

cold_start_n_iters: int | None: Number of iterations to run optimization during the first instance. Setting to None will use n_iters. This parameter is useful in MPC like settings where we need to run many iterations during initialization (cold start) and then run only few iterations (warm start).

rollout_fn: RolloutBase: Rollout function to use for computing cost, given optimization variables.

tensor_args: TensorDeviceType: Tensor device to use for optimization.

use_cuda_graph: bool: Capture optimization iteration in a cuda graph and replay graph instead of eager execution. Enabling this can make optimization 10x faster. But changing control flow, tensor shapes, or problem type is not allowed.

store_debug: bool: Record debugging data such as optimization variables, and cost at every iteration. Enabling this will disable cuda graph.

debug_info: Any: Use this to record additional attributes from rollouts.

n_problems: int: Number of parallel problems to optimize.

num_particles: int | None: Number of particles to use per problem. Common optimization solvers use many particles to optimize a single problem. E.g., MPPI rolls out many parallel samples and computes a weighted mean. In cuRobo, Quasi-Newton solvers use particles to run many line search magnitudes. Total optimization batch size = n_problems * num_particles.

sync_cuda_time: bool: Synchronize device before computing solver time.

use_coo_sparse: bool: Matmul with a Sparse tensor is used to create particles for each problem index to save memory and compute. Some versions of pytorch do not support coo sparse, specifically during torch profile runs. Set this to False to use a standard tensor.