OALegiblePathQRCost.py

import os
import sys
module_path = os.path.abspath(os.path.join('../ilqr'))
if module_path not in sys.path:
    sys.path.append(module_path)

import numpy as np
import matplotlib.pyplot as plt
import decimal
import copy

from ilqr import iLQR
from ilqr.cost import Cost
from ilqr.cost import QRCost
from ilqr.cost import PathQRCost, AutoDiffCost, FiniteDiffCost
from ilqr.dynamics import constrain
from ilqr.examples.pendulum import InvertedPendulumDynamics
from ilqr.dynamics import BatchAutoDiffDynamics, tensor_constrain

from scipy.optimize import approx_fprime

import utility_legibility as legib
import utility_environ_descrip as resto
import pipeline_generate_paths as pipeline
import pdb

from LegiblePathQRCost import LegiblePathQRCost
import PathingExperiment as ex

class OALegiblePathQRCost(LegiblePathQRCost):
    FLAG_DEBUG_J = False
    FLAG_DEBUG_STAGE_AND_TERM = True

    """Quadratic Regulator Instantaneous Cost for trajectory following."""
    def __init__(self, exp, x_path, u_path):
        self.make_self(
            exp,
            exp.get_Q(),
            exp.get_R(),
            exp.get_Qf(),
            x_path,
            u_path,
            exp.get_start(),
            exp.get_target_goal(),
            exp.get_goals(),
            exp.get_N(),
            exp.get_dt(),
            restaurant=exp.get_restaurant(),
            file_id=exp.get_file_id()
            )


    def make_self(self, exp, Q, R, Qf, x_path, u_path, start, target_goal, goals, N, dt, restaurant=None, file_id=None, Q_terminal=None):
        """Constructs a QRCost.
        Args:
            Q: Quadratic state cost matrix [state_size, state_size].
            R: Quadratic control cost matrix [action_size, action_size].
            x_path: Goal state path [N+1, state_size].
            u_path: Goal control path [N, action_size].
            Q_terminal: Terminal quadratic state cost matrix
                [state_size, state_size].
        """

        LegiblePathQRCost.__init__(
            self, exp, x_path, u_path
        )


    # # How far away is the final step in the path from the goal?
    # def term_cost(self, x, i):
    #     start = self.start
    #     goal1 = self.target_goal
        
    #     # Qf = self.Q_terminal
    #     Qf = self.Qf
    #     R = self.R

    #     # x_diff = x - self.x_path[i]
    #     x_diff = x - self.x_path[self.N]
    #     squared_x_cost = .5 * x_diff.T.dot(Qf).dot(x_diff)
    #     # squared_x_cost = squared_x_cost * squared_x_cost

    #     terminal_cost = squared_x_cost

    #     if self.FLAG_DEBUG_STAGE_AND_TERM:
    #         print("term cost squared x cost")
    #         print(squared_x_cost)

    #     # We want to value this highly enough that we don't not end at the goal
    #     # terminal_coeff = 100.0
    #     coeff_terminal  = self.exp.get_coeff_terminal()
    #     terminal_cost   = terminal_cost * coeff_terminal

    #     return terminal_cost

    # original version for plain path following
    def l(self, x, u, i, terminal=False, just_term=False, just_stage=False):
        """Instantaneous cost function.
        Args:
            x: Current state [state_size].
            u: Current control [action_size]. None if terminal.
            i: Current time step.
            terminal: Compute terminal cost. Default: False.
        Returns:
            Instantaneous cost (scalar).
        """
        Q = self.Qf if terminal else self.Q
        R = self.R
        start = self.start
        goal = self.target_goal
        thresh = .0001

        scale_term = self.exp.get_solver_scale_term() #0.01 # 1/100
        scale_stage = self.exp.get_solver_scale_stage() #1.5

        if just_term:   
            scale_stage = 0

        if just_stage:
            scale_term  = 0

        term_cost = 0 #self.term_cost(x, i)

        # x_diff is compared to reference path
        x_diff = x - self.x_path[i]

        u_diff = 0
        if i < len(self.u_path):
            u_diff = u - self.u_path[i]

        if terminal or just_term: #abs(i - self.N) < thresh or
            # TODO verify not a magic number
            return scale_term * self.term_cost(x, i) # * 1000
        else:
            if self.FLAG_DEBUG_STAGE_AND_TERM:
                # difference between this step and the end
                print("x, N, x_end_of_path -> inputs and then term cost")
                print(x, self.N, self.x_path[self.N])
                # term_cost = self.term_cost(x, i)
                print(term_cost)

        # VISIBILITY COMPONENT
        restaurant  = self.exp.get_restaurant()
        observers   = restaurant.get_observers()

        visibility  = legib.get_visibility_of_pt_w_observers_ilqr(x, observers, normalized=True)
        FLAG_OA_MIN_VIS = False

        # if FLAG_OA_MIN_VIS:
        #     if visibility == 0:
        #         visibility = .01

        # f_func     = self.get_f()
        # f_value    = f_func(i)

        f_func     = self.get_f()
        f_value    = visibility

        # KEEP THE VIS VALUE IF F_VIS_LIN, OR...
        if self.exp.get_f_label() is ex.F_VIS_BIN:
            if f_value > 0:
                f_value = 1.0
            else:
                f_value = 0.0
        elif self.exp.get_f_label() is ex.F_NONE:
            f_value = 1.0

        # J does not need to be in a particular range, it can be any max or min
        J = self.michelle_stage_cost(start, goal, x, u, i, terminal) * f_value



        wt_legib     = 100.0
        wt_lam       = .0001
        wt_control   = 0 #10.0

        J =  (wt_legib       * J)
        J += (wt_control    * u_diff.T.dot(R).dot(u_diff))
        J += (wt_lam        * x_diff.T.dot(Q).dot(x_diff))

    
        stage_costs = J

        if self.FLAG_DEBUG_STAGE_AND_TERM:
            print("STAGE,\t TERM")
            print(stage_costs, term_cost)

        total = (scale_term * term_cost) + (scale_stage * stage_costs)

        return float(total)

    # # original version for plain path following
    # def l(self, x, u, i, terminal=False, just_term=False, just_stage=False):
    #     """Instantaneous cost function.
    #     Args:
    #         x: Current state [state_size].
    #         u: Current control [action_size]. None if terminal.
    #         i: Current time step.
    #         terminal: Compute terminal cost. Default: False.
    #     Returns:
    #         Instantaneous cost (scalar).
    #     """
    #     Q = self.Qf if terminal else self.Q
    #     R = self.R
    #     start = self.start
    #     goal = self.target_goal
    #     thresh = .0001

    #     term_cost = 0

    #     if terminal or just_term: # or abs(i - self.N) < thresh:
    #         return self.term_cost(x, i) #*1000
    #     else:
    #         if self.FLAG_DEBUG_STAGE_AND_TERM:
    #             # difference between this step and the end
    #             print("x, N, x_end_of_path -> inputs and then term cost")
    #             print(x, self.N, self.x_path[self.N])
    #             # term_cost = self.term_cost(x, i)


    #     # VISIBILITY COMPONENT
    #     restaurant  = self.exp.get_restaurant()
    #     observers   = restaurant.get_observers()

    #     visibility  = 1 #legib.get_visibility_of_pt_w_observers_ilqr(x, observers, normalized=True)
    #     FLAG_OA_MIN_VIS = False

    #     if FLAG_OA_MIN_VIS:
    #         if visibility == 0:
    #             visibility = .01

    #     # f_func     = self.get_f()
    #     # f_value    = f_func(i)

    #     # if f_value != visibility:
    #     #     exit()

    #     f_value    = visibility

    #     # PATH COST PENALTY COMPONENT
    #     x_diff = x - self.x_path[self.N]
    #     Q_path_cost = np.identity(2)
    #     path_squared_x_cost = .5 * x_diff.T.dot(Q_path_cost).dot(x_diff)

    #     stage_costs = self.michelle_stage_cost(start, goal, x, u, i, terminal) * f_value
    #     stage_costs = stage_costs + self.exp.get_lambda_cost_path_coeff() * path_squared_x_cost
    
    #     if self.FLAG_DEBUG_STAGE_AND_TERM:
    #         print("STAGE,\t TERM")
    #         print(stage_costs, term_cost)

    #     # Log of remixes of term and stage cost weightings
    #     # term_cost      = decimal.Decimal.ln(decimal.Decimal(term_cost)) 
    #     # stage_costs    = decimal.Decimal.ln(stage_costs)
    #     # if i < 30:
    #     #     stage_scale = 200
    #     #     term_scale = 0.1
    #     # else:
    #     #     stage_scale = 10
    #     #     term_scale = 1
    #     # stage_scale = max([(self.N - i), 20])
    #     # term_scale = 100
    #     # stage_scale = 10
    #     # term_scale = 1
    #     # stage_scale = max([self.N-i, 10])
    #     # stage_scale = abs(self.N-i)
    #     # term_scale = i/self.N
    #     # term_scale = 1
    #     # stage_scale = 50

    #     scale_term  = self.scale_term #0.01 # 1/100
    #     scale_stage = self.scale_stage #1.5

    #     if just_term:   
    #         scale_stage = 0

    #     if just_stage:
    #         scale_term  = 0

    #     total = (scale_term * term_cost) + (scale_stage * stage_costs)

    #     return float(total)

    def f(t):
        return 1.0

    def get_total_stage_cost(self, start, goal, x, u, i, terminal):
        N = self.N
        R = self.R

        stage_costs = 0.0 #u_diff.T.dot(R).dot(u_diff)
        
        if self.FLAG_DEBUG_STAGE_AND_TERM:
            print("u = " + str(u))
            print("Getting stage cost")

        for j in range(i):
            u_diff = u - self.u_path[j]

            stage_costs += (0.5 * u_diff.T.dot(R).dot(u_diff))

        if self.FLAG_DEBUG_STAGE_AND_TERM:
            print("total stage cost " + str(stage_costs))

        return stage_costs

    def michelle_stage_cost(self, start, goal, x, u, i, terminal=False):
        Q = self.Q_terminal if terminal else self.Q
        R = self.R

        all_goals = self.goals

        goal_diff   = start - goal
        start_diff  = (start - np.array(x))
        togoal_diff = (np.array(x) - goal)

        u_diff = u - self.u_path[i]

        if len(self.u_path) == 0:
            return 0

        a = (goal_diff.T).dot(Q).dot((goal_diff))
        b = (start_diff.T).dot(Q).dot((start_diff))
        c = (togoal_diff.T).dot(Q).dot((togoal_diff))

        # (start-goal1)'*Q*(start-goal1) - (start-x)'*Q*(start-x) +  - (x-goal1)'*Q*(x-goal1) 
        J_g1 = a - b - c
        # J_g1 *= .5

        if self.FLAG_DEBUG_STAGE_AND_TERM:
            print("For point at x -> " + str(x))
            # print("Jg1 " + str(J_g1))

        log_sum = 0.0
        total_sum = 0.0
        for alt_goal in all_goals:
            # n = - ((start-x)'*Q*(start-x) + 5) - ((x-goal)'*Q*(x-goal)+10)
            # d = (start-goal)'*Q*(start-goal)
            # log_sum += (exp(n )/exp(d))* scale

            
            diff_curr   = start - x
            diff_goal   = x - alt_goal
            diff_all    = start - alt_goal

            diff_curr   = diff_curr.T
            diff_goal   = diff_goal.T
            diff_all    = diff_all.T

            n = - (diff_curr.T).dot(Q).dot((diff_curr)) - ((diff_goal).T.dot(Q).dot(diff_goal))
            d = (diff_all).T.dot(Q).dot(diff_all)

            this_goal = np.exp(n) / np.exp(d)

            total_sum += this_goal

            if goal != alt_goal:
                log_sum += (1 * this_goal)
                if self.FLAG_DEBUG_STAGE_AND_TERM:
                    # print("Value for alt target goal " + str(alt_goal))
                    print("This is the nontarget goal: " + str(alt_goal) + " -> " + str(this_goal))
            else:
                # print("Value for our target goal " + str(goal))
                # J_g1 = this_goal
                log_sum += this_goal
                if self.FLAG_DEBUG_STAGE_AND_TERM:
                    print("This is the target goal " + str(alt_goal) + " -> " + str(this_goal))
    

        if self.FLAG_DEBUG_STAGE_AND_TERM:
            print("Jg1, total")
            print(J_g1, total_sum)

        J = J_g1 - (np.log(total_sum))
        J = -1.0 * J

        if self.FLAG_DEBUG_STAGE_AND_TERM:
            print("overall J " + str(J))

        J += (0.5 * u_diff.T.dot(R).dot(u_diff))

        # # We want the path to be smooth, so we incentivize small and distributed u

        return J

    def stage_cost(self, x, u, i, terminal=False):
        print("DOING STAGE COST")
        start   = self.start
        goal    = self.target_goal

        x = np.array(x)
        J = self.goal_efficiency_through_point_relative(start, goal, x, terminal)
        return J