ddp.cpp

 
// BSD 3-Clause License
//
// Copyright (C) 2019-2022, LAAS-CNRS, University of Edinburgh,
//                          University of Oxford, Heriot-Watt University
// Copyright note valid unless otherwise stated in individual files.
// All rights reserved.
 
#include "crocoddyl/core/solvers/ddp.hpp"
 
#include <iostream>
 
#include "crocoddyl/core/utils/exception.hpp"
 
namespace crocoddyl {
 
SolverDDP::SolverDDP(std::shared_ptr<ShootingProblem> problem)
    : SolverAbstract(problem),
      reg_incfactor_(10.),
      reg_decfactor_(10.),
      reg_min_(1e-9),
      reg_max_(1e9),
      cost_try_(0.),
      th_grad_(1e-12),
      th_stepdec_(0.5),
      th_stepinc_(0.01) {
  allocateData();
 
  const std::size_t n_alphas = 10;
  alphas_.resize(n_alphas);
  for (std::size_t n = 0; n < n_alphas; ++n) {
    alphas_[n] = 1. / pow(2., static_cast<double>(n));
  }
  if (th_stepinc_ < alphas_[n_alphas - 1]) {
    th_stepinc_ = alphas_[n_alphas - 1];
    std::cerr << "Warning: th_stepinc has higher value than lowest alpha "
                 "value, set to "
              << std::to_string(alphas_[n_alphas - 1]) << std::endl;
  }
}
 
SolverDDP::~SolverDDP() {}
 
bool SolverDDP::solve(const std::vector<Eigen::VectorXd>& init_xs,
                      const std::vector<Eigen::VectorXd>& init_us,
                      const std::size_t maxiter, const bool is_feasible,
                      const double init_reg) {
  START_PROFILER("SolverDDP::solve");
  if (problem_->is_updated()) {
    resizeData();
  }
  xs_try_[0] =
      problem_->get_x0();  // it is needed in case that init_xs[0] is infeasible
  setCandidate(init_xs, init_us, is_feasible);
 
  if (std::isnan(init_reg)) {
    preg_ = reg_min_;
    dreg_ = reg_min_;
  } else {
    preg_ = init_reg;
    dreg_ = init_reg;
  }
  was_feasible_ = false;
 
  bool recalcDiff = true;
  for (iter_ = 0; iter_ < maxiter; ++iter_) {
    while (true) {
      try {
        computeDirection(recalcDiff);
      } catch (std::exception& e) {
        recalcDiff = false;
        increaseRegularization();
        if (preg_ == reg_max_) {
          return false;
        } else {
          continue;
        }
      }
      break;
    }
    expectedImprovement();
 
    // We need to recalculate the derivatives when the step length passes
    recalcDiff = false;
    for (std::vector<double>::const_iterator it = alphas_.begin();
         it != alphas_.end(); ++it) {
      steplength_ = *it;
 
      try {
        dV_ = tryStep(steplength_);
      } catch (std::exception& e) {
        continue;
      }
      dVexp_ = steplength_ * (d_[0] + 0.5 * steplength_ * d_[1]);
 
      if (dVexp_ >= 0) {  // descend direction
        if (std::abs(d_[0]) < th_grad_ || !is_feasible_ ||
            dV_ > th_acceptstep_ * dVexp_) {
          was_feasible_ = is_feasible_;
          setCandidate(xs_try_, us_try_, true);
          cost_ = cost_try_;
          recalcDiff = true;
          break;
        }
      }
    }
 
    if (steplength_ > th_stepdec_) {
      decreaseRegularization();
    }
    if (steplength_ <= th_stepinc_) {
      increaseRegularization();
      if (preg_ == reg_max_) {
        STOP_PROFILER("SolverDDP::solve");
        return false;
      }
    }
    stoppingCriteria();
 
    const std::size_t n_callbacks = callbacks_.size();
    for (std::size_t c = 0; c < n_callbacks; ++c) {
      CallbackAbstract& callback = *callbacks_[c];
      callback(*this);
    }
 
    if (was_feasible_ && stop_ < th_stop_) {
      STOP_PROFILER("SolverDDP::solve");
      return true;
    }
  }
  STOP_PROFILER("SolverDDP::solve");
  return false;
}
 
void SolverDDP::computeDirection(const bool recalcDiff) {
  START_PROFILER("SolverDDP::computeDirection");
  if (recalcDiff) {
    calcDiff();
  }
  backwardPass();
  STOP_PROFILER("SolverDDP::computeDirection");
}
 
double SolverDDP::tryStep(const double steplength) {
  START_PROFILER("SolverDDP::tryStep");
  forwardPass(steplength);
  STOP_PROFILER("SolverDDP::tryStep");
  return cost_ - cost_try_;
}
 
double SolverDDP::stoppingCriteria() {
  // This stopping criteria represents the expected reduction in the value
  // function. If this reduction is less than a certain threshold, then the
  // algorithm reaches the local minimum. For more details, see C. Mastalli et
  // al. "Inverse-dynamics MPC via Nullspace Resolution".
  stop_ = std::abs(d_[0] + 0.5 * d_[1]);
  return stop_;
}
 
const Eigen::Vector2d& SolverDDP::expectedImprovement() {
  d_.fill(0);
  const std::size_t T = this->problem_->get_T();
  const std::vector<std::shared_ptr<ActionModelAbstract> >& models =
      problem_->get_runningModels();
  for (std::size_t t = 0; t < T; ++t) {
    const std::size_t nu = models[t]->get_nu();
    if (nu != 0) {
      d_[0] += Qu_[t].dot(k_[t]);
      d_[1] -= k_[t].dot(Quuk_[t]);
    }
  }
  return d_;
}
 
void SolverDDP::resizeData() {
  START_PROFILER("SolverDDP::resizeData");
  SolverAbstract::resizeData();
 
  const std::size_t T = problem_->get_T();
  const std::size_t ndx = problem_->get_ndx();
  const std::vector<std::shared_ptr<ActionModelAbstract> >& models =
      problem_->get_runningModels();
  for (std::size_t t = 0; t < T; ++t) {
    const std::shared_ptr<ActionModelAbstract>& model = models[t];
    const std::size_t nu = model->get_nu();
    Qxu_[t].conservativeResize(ndx, nu);
    Quu_[t].conservativeResize(nu, nu);
    Qu_[t].conservativeResize(nu);
    K_[t].conservativeResize(nu, ndx);
    k_[t].conservativeResize(nu);
    us_try_[t].conservativeResize(nu);
    FuTVxx_p_[t].conservativeResize(nu, ndx);
    Quuk_[t].conservativeResize(nu);
    if (nu != 0) {
      FuTVxx_p_[t].setZero();
    }
  }
  STOP_PROFILER("SolverDDP::resizeData");
}
 
double SolverDDP::calcDiff() {
  START_PROFILER("SolverDDP::calcDiff");
  if (iter_ == 0) {
    problem_->calc(xs_, us_);
  }
  cost_ = problem_->calcDiff(xs_, us_);
 
  ffeas_ = computeDynamicFeasibility();
  gfeas_ = computeInequalityFeasibility();
  hfeas_ = computeEqualityFeasibility();
  STOP_PROFILER("SolverDDP::calcDiff");
  return cost_;
}
 
void SolverDDP::backwardPass() {
  START_PROFILER("SolverDDP::backwardPass");
  const std::shared_ptr<ActionDataAbstract>& d_T = problem_->get_terminalData();
  Vxx_.back() = d_T->Lxx;
  Vx_.back() = d_T->Lx;
 
  if (!std::isnan(preg_)) {
    Vxx_.back().diagonal().array() += preg_;
  }
 
  if (!is_feasible_) {
    Vx_.back().noalias() += Vxx_.back() * fs_.back();
  }
  const std::vector<std::shared_ptr<ActionModelAbstract> >& models =
      problem_->get_runningModels();
  const std::vector<std::shared_ptr<ActionDataAbstract> >& datas =
      problem_->get_runningDatas();
  for (int t = static_cast<int>(problem_->get_T()) - 1; t >= 0; --t) {
    const std::shared_ptr<ActionModelAbstract>& m = models[t];
    const std::shared_ptr<ActionDataAbstract>& d = datas[t];
 
    // Compute the linear-quadratic approximation of the control Hamiltonian
    // function
    computeActionValueFunction(t, m, d);
 
    // Compute the feedforward and feedback gains
    computeGains(t);
 
    // Compute the linear-quadratic approximation of the Value function
    computeValueFunction(t, m);
 
    if (raiseIfNaN(Vx_[t].lpNorm<Eigen::Infinity>())) {
      throw_pretty("backward_error");
    }
    if (raiseIfNaN(Vxx_[t].lpNorm<Eigen::Infinity>())) {
      throw_pretty("backward_error");
    }
  }
  STOP_PROFILER("SolverDDP::backwardPass");
}
 
void SolverDDP::forwardPass(const double steplength) {
  if (steplength > 1. || steplength < 0.) {
    throw_pretty("Invalid argument: "
                 << "invalid step length, value is between 0. to 1.");
  }
  START_PROFILER("SolverDDP::forwardPass");
  cost_try_ = 0.;
  const std::size_t T = problem_->get_T();
  const std::vector<std::shared_ptr<ActionModelAbstract> >& models =
      problem_->get_runningModels();
  const std::vector<std::shared_ptr<ActionDataAbstract> >& datas =
      problem_->get_runningDatas();
  for (std::size_t t = 0; t < T; ++t) {
    const std::shared_ptr<ActionModelAbstract>& m = models[t];
    const std::shared_ptr<ActionDataAbstract>& d = datas[t];
 
    m->get_state()->diff(xs_[t], xs_try_[t], dx_[t]);
    if (m->get_nu() != 0) {
      us_try_[t].noalias() = us_[t];
      us_try_[t].noalias() -= k_[t] * steplength;
      us_try_[t].noalias() -= K_[t] * dx_[t];
      m->calc(d, xs_try_[t], us_try_[t]);
    } else {
      m->calc(d, xs_try_[t]);
    }
    xs_try_[t + 1] = d->xnext;
    cost_try_ += d->cost;
 
    if (raiseIfNaN(cost_try_)) {
      STOP_PROFILER("SolverDDP::forwardPass");
      throw_pretty("forward_error");
    }
    if (raiseIfNaN(xs_try_[t + 1].lpNorm<Eigen::Infinity>())) {
      STOP_PROFILER("SolverDDP::forwardPass");
      throw_pretty("forward_error");
    }
  }
 
  const std::shared_ptr<ActionModelAbstract>& m = problem_->get_terminalModel();
  const std::shared_ptr<ActionDataAbstract>& d = problem_->get_terminalData();
  m->calc(d, xs_try_.back());
  cost_try_ += d->cost;
 
  if (raiseIfNaN(cost_try_)) {
    STOP_PROFILER("SolverDDP::forwardPass");
    throw_pretty("forward_error");
  }
  STOP_PROFILER("SolverDDP::forwardPass");
}
 
void SolverDDP::computeActionValueFunction(
    const std::size_t t, const std::shared_ptr<ActionModelAbstract>& model,
    const std::shared_ptr<ActionDataAbstract>& data) {
  assert_pretty(t < problem_->get_T(),
                "Invalid argument: t should be between 0 and " +
                    std::to_string(problem_->get_T()););
  const std::size_t nu = model->get_nu();
  const Eigen::MatrixXd& Vxx_p = Vxx_[t + 1];
  const Eigen::VectorXd& Vx_p = Vx_[t + 1];
 
  FxTVxx_p_.noalias() = data->Fx.transpose() * Vxx_p;
  START_PROFILER("SolverDDP::Qx");
  Qx_[t] = data->Lx;
  Qx_[t].noalias() += data->Fx.transpose() * Vx_p;
  STOP_PROFILER("SolverDDP::Qx");
  START_PROFILER("SolverDDP::Qxx");
  Qxx_[t] = data->Lxx;
  Qxx_[t].noalias() += FxTVxx_p_ * data->Fx;
  STOP_PROFILER("SolverDDP::Qxx");
  if (nu != 0) {
    FuTVxx_p_[t].noalias() = data->Fu.transpose() * Vxx_p;
    START_PROFILER("SolverDDP::Qu");
    Qu_[t] = data->Lu;
    Qu_[t].noalias() += data->Fu.transpose() * Vx_p;
    STOP_PROFILER("SolverDDP::Qu");
    START_PROFILER("SolverDDP::Quu");
    Quu_[t] = data->Luu;
    Quu_[t].noalias() += FuTVxx_p_[t] * data->Fu;
    STOP_PROFILER("SolverDDP::Quu");
    START_PROFILER("SolverDDP::Qxu");
    Qxu_[t] = data->Lxu;
    Qxu_[t].noalias() += FxTVxx_p_ * data->Fu;
    STOP_PROFILER("SolverDDP::Qxu");
    if (!std::isnan(preg_)) {
      Quu_[t].diagonal().array() += preg_;
    }
  }
}
 
void SolverDDP::computeValueFunction(
    const std::size_t t, const std::shared_ptr<ActionModelAbstract>& model) {
  assert_pretty(t < problem_->get_T(),
                "Invalid argument: t should be between 0 and " +
                    std::to_string(problem_->get_T()););
  const std::size_t nu = model->get_nu();
  Vx_[t] = Qx_[t];
  Vxx_[t] = Qxx_[t];
  if (nu != 0) {
    START_PROFILER("SolverDDP::Vx");
    Quuk_[t].noalias() = Quu_[t] * k_[t];
    Vx_[t].noalias() -= K_[t].transpose() * Qu_[t];
    STOP_PROFILER("SolverDDP::Vx");
    START_PROFILER("SolverDDP::Vxx");
    Vxx_[t].noalias() -= Qxu_[t] * K_[t];
    STOP_PROFILER("SolverDDP::Vxx");
  }
  Vxx_tmp_ = 0.5 * (Vxx_[t] + Vxx_[t].transpose());
  Vxx_[t] = Vxx_tmp_;
 
  if (!std::isnan(preg_)) {
    Vxx_[t].diagonal().array() += preg_;
  }
 
  // Compute and store the Vx gradient at end of the interval (rollout state)
  if (!is_feasible_) {
    Vx_[t].noalias() += Vxx_[t] * fs_[t];
  }
}
 
void SolverDDP::computeGains(const std::size_t t) {
  assert_pretty(t < problem_->get_T(),
                "Invalid argument: t should be between 0 and " +
                    std::to_string(problem_->get_T()));
  START_PROFILER("SolverDDP::computeGains");
  const std::size_t nu = problem_->get_runningModels()[t]->get_nu();
  if (nu > 0) {
    START_PROFILER("SolverDDP::Quu_inv");
    Quu_llt_[t].compute(Quu_[t]);
    STOP_PROFILER("SolverDDP::Quu_inv");
    const Eigen::ComputationInfo& info = Quu_llt_[t].info();
    if (info != Eigen::Success) {
      STOP_PROFILER("SolverDDP::computeGains");
      throw_pretty("backward_error");
    }
    K_[t] = Qxu_[t].transpose();
 
    START_PROFILER("SolverDDP::Quu_inv_Qux");
    Quu_llt_[t].solveInPlace(K_[t]);
    STOP_PROFILER("SolverDDP::Quu_inv_Qux");
    k_[t] = Qu_[t];
    Quu_llt_[t].solveInPlace(k_[t]);
  }
  STOP_PROFILER("SolverDDP::computeGains");
}
 
void SolverDDP::increaseRegularization() {
  preg_ *= reg_incfactor_;
  if (preg_ > reg_max_) {
    preg_ = reg_max_;
  }
  dreg_ = preg_;
}
 
void SolverDDP::decreaseRegularization() {
  preg_ /= reg_decfactor_;
  if (preg_ < reg_min_) {
    preg_ = reg_min_;
  }
  dreg_ = preg_;
}
 
void SolverDDP::allocateData() {
  const std::size_t T = problem_->get_T();
  Vxx_.resize(T + 1);
  Vx_.resize(T + 1);
  Qxx_.resize(T);
  Qxu_.resize(T);
  Quu_.resize(T);
  Qx_.resize(T);
  Qu_.resize(T);
  K_.resize(T);
  k_.resize(T);
 
  xs_try_.resize(T + 1);
  us_try_.resize(T);
  dx_.resize(T);
 
  FuTVxx_p_.resize(T);
  Quu_llt_.resize(T);
  Quuk_.resize(T);
 
  const std::size_t ndx = problem_->get_ndx();
  const std::vector<std::shared_ptr<ActionModelAbstract> >& models =
      problem_->get_runningModels();
  for (std::size_t t = 0; t < T; ++t) {
    const std::shared_ptr<ActionModelAbstract>& model = models[t];
    const std::size_t nu = model->get_nu();
    Vxx_[t] = Eigen::MatrixXd::Zero(ndx, ndx);
    Vx_[t] = Eigen::VectorXd::Zero(ndx);
    Qxx_[t] = Eigen::MatrixXd::Zero(ndx, ndx);
    Qxu_[t] = Eigen::MatrixXd::Zero(ndx, nu);
    Quu_[t] = Eigen::MatrixXd::Zero(nu, nu);
    Qx_[t] = Eigen::VectorXd::Zero(ndx);
    Qu_[t] = Eigen::VectorXd::Zero(nu);
    K_[t] = MatrixXdRowMajor::Zero(nu, ndx);
    k_[t] = Eigen::VectorXd::Zero(nu);
 
    if (t == 0) {
      xs_try_[t] = problem_->get_x0();
    } else {
      xs_try_[t] = model->get_state()->zero();
    }
    us_try_[t] = Eigen::VectorXd::Zero(nu);
    dx_[t] = Eigen::VectorXd::Zero(ndx);
 
    FuTVxx_p_[t] = MatrixXdRowMajor::Zero(nu, ndx);
    Quu_llt_[t] = Eigen::LLT<Eigen::MatrixXd>(nu);
    Quuk_[t] = Eigen::VectorXd(nu);
  }
  Vxx_.back() = Eigen::MatrixXd::Zero(ndx, ndx);
  Vxx_tmp_ = Eigen::MatrixXd::Zero(ndx, ndx);
  Vx_.back() = Eigen::VectorXd::Zero(ndx);
  xs_try_.back() = problem_->get_terminalModel()->get_state()->zero();
 
  FxTVxx_p_ = MatrixXdRowMajor::Zero(ndx, ndx);
  fTVxx_p_ = Eigen::VectorXd::Zero(ndx);
}
 
double SolverDDP::get_reg_incfactor() const { return reg_incfactor_; }
 
double SolverDDP::get_reg_decfactor() const { return reg_decfactor_; }
 
double SolverDDP::get_regfactor() const { return reg_incfactor_; }
 
double SolverDDP::get_reg_min() const { return reg_min_; }
 
double SolverDDP::get_regmin() const { return reg_min_; }
 
double SolverDDP::get_reg_max() const { return reg_max_; }
 
double SolverDDP::get_regmax() const { return reg_max_; }
 
const std::vector<double>& SolverDDP::get_alphas() const { return alphas_; }
 
double SolverDDP::get_th_stepdec() const { return th_stepdec_; }
 
double SolverDDP::get_th_stepinc() const { return th_stepinc_; }
 
double SolverDDP::get_th_grad() const { return th_grad_; }
 
const std::vector<Eigen::MatrixXd>& SolverDDP::get_Vxx() const { return Vxx_; }
 
const std::vector<Eigen::VectorXd>& SolverDDP::get_Vx() const { return Vx_; }
 
const std::vector<Eigen::MatrixXd>& SolverDDP::get_Qxx() const { return Qxx_; }
 
const std::vector<Eigen::MatrixXd>& SolverDDP::get_Qxu() const { return Qxu_; }
 
const std::vector<Eigen::MatrixXd>& SolverDDP::get_Quu() const { return Quu_; }
 
const std::vector<Eigen::VectorXd>& SolverDDP::get_Qx() const { return Qx_; }
 
const std::vector<Eigen::VectorXd>& SolverDDP::get_Qu() const { return Qu_; }
 
const std::vector<typename MathBaseTpl<double>::MatrixXsRowMajor>&
SolverDDP::get_K() const {
  return K_;
}
 
const std::vector<Eigen::VectorXd>& SolverDDP::get_k() const { return k_; }
 
void SolverDDP::set_reg_incfactor(const double regfactor) {
  if (regfactor <= 1.) {
    throw_pretty(
        "Invalid argument: " << "reg_incfactor value is higher than 1.");
  }
  reg_incfactor_ = regfactor;
}
 
void SolverDDP::set_reg_decfactor(const double regfactor) {
  if (regfactor <= 1.) {
    throw_pretty(
        "Invalid argument: " << "reg_decfactor value is higher than 1.");
  }
  reg_decfactor_ = regfactor;
}
 
void SolverDDP::set_regfactor(const double regfactor) {
  if (regfactor <= 1.) {
    throw_pretty("Invalid argument: " << "regfactor value is higher than 1.");
  }
  set_reg_incfactor(regfactor);
  set_reg_decfactor(regfactor);
}
 
void SolverDDP::set_reg_min(const double regmin) {
  if (0. > regmin) {
    throw_pretty("Invalid argument: " << "regmin value has to be positive.");
  }
  reg_min_ = regmin;
}
 
void SolverDDP::set_regmin(const double regmin) {
  if (0. > regmin) {
    throw_pretty("Invalid argument: " << "regmin value has to be positive.");
  }
  reg_min_ = regmin;
}
 
void SolverDDP::set_reg_max(const double regmax) {
  if (0. > regmax) {
    throw_pretty("Invalid argument: " << "regmax value has to be positive.");
  }
  reg_max_ = regmax;
}
 
void SolverDDP::set_regmax(const double regmax) {
  if (0. > regmax) {
    throw_pretty("Invalid argument: " << "regmax value has to be positive.");
  }
  reg_max_ = regmax;
}
 
void SolverDDP::set_alphas(const std::vector<double>& alphas) {
  double prev_alpha = alphas[0];
  if (prev_alpha != 1.) {
    std::cerr << "Warning: alpha[0] should be 1" << std::endl;
  }
  for (std::size_t i = 1; i < alphas.size(); ++i) {
    double alpha = alphas[i];
    if (0. >= alpha) {
      throw_pretty("Invalid argument: " << "alpha values has to be positive.");
    }
    if (alpha >= prev_alpha) {
      throw_pretty(
          "Invalid argument: " << "alpha values are monotonously decreasing.");
    }
    prev_alpha = alpha;
  }
  alphas_ = alphas;
}
 
void SolverDDP::set_th_stepdec(const double th_stepdec) {
  if (0. >= th_stepdec || th_stepdec > 1.) {
    throw_pretty(
        "Invalid argument: " << "th_stepdec value should between 0 and 1.");
  }
  th_stepdec_ = th_stepdec;
}
 
void SolverDDP::set_th_stepinc(const double th_stepinc) {
  if (0. >= th_stepinc || th_stepinc > 1.) {
    throw_pretty(
        "Invalid argument: " << "th_stepinc value should between 0 and 1.");
  }
  th_stepinc_ = th_stepinc;
}
 
void SolverDDP::set_th_grad(const double th_grad) {
  if (0. > th_grad) {
    throw_pretty("Invalid argument: " << "th_grad value has to be positive.");
  }
  th_grad_ = th_grad;
}
 
}  // namespace crocoddyl