serser

# -----------
# User Instructions
#
# Implement a P controller by running 100 iterations
# of robot motion. The steering angle should be set
# by the parameter tau so that:
#
# steering = -tau_p * CTE - tau_d * diff_CTE - tau_i * int_CTE
#
# where the integrated crosstrack error (int_CTE) is

serser

# -----------
# User Instructions
#
# Implement a PD controller by running 100 iterations
# of robot motion. The steering angle should be set
# by the parameter tau so that:
#
# steering = -tau_p * CTE - tau_d * diff_CTE
# where differential crosstrack error (diff_CTE)
# is given by CTE(t) - CTE(t-1)

serser

# https://classroom.udacity.com/courses/cs373/lessons/48743150/concepts/487372200923
# -----------
# User Instructions
#
# Implement a P controller by running 100 iterations
# of robot motion. The desired trajectory for the 
# robot is the x-axis. The steering angle should be set
# by the parameter tau so that:
#
# steering = -tau * crosstrack_error

serser

# https://classroom.udacity.com/courses/cs373/lessons/48743150/concepts/487471330923
# -----------
# User Instructions
#
# Define a function smooth that takes a path as its input
# (with optional parameters for weight_data, weight_smooth,
# and tolerance) and returns a smooth path. The first and 
# last points should remain unchanged.
#
# Smoothing should be implemented by iteratively updating

serser

# https://classroom.udacity.com/courses/cs373/lessons/48532756/concepts/487024740923
# --------------
# USER INSTRUCTIONS
#
# Write a function called stochastic_value that 
# returns two grids. The first grid, value, should 
# contain the computed value of each cell as shown 
# in the video. The second grid, policy, should 
# contain the optimum policy for each cell.
#

serser

# ----------
# User Instructions:
# 
# Implement the function optimum_policy2D below.
#
# You are given a car in grid with initial state
# init. Your task is to compute and return the car's 
# optimal path to the position specified in goal; 
# the costs for each motion are as defined in cost.
#

serser

# https://classroom.udacity.com/courses/cs373/lessons/48646841/concepts/485327600923
# ----------
# User Instructions:
# 
# Write a function optimum_policy that returns
# a grid which shows the optimum policy for robot
# motion. This means there should be an optimum
# direction associated with each navigable cell from
# which the goal can be reached.
# 

serser

# https://classroom.udacity.com/courses/cs373/lessons/48646841/concepts/487174190923
# populate the value of grid starting from the goal.
# ----------
# User Instructions:
# 
# Create a function compute_value which returns
# a grid of values. The value of a cell is the minimum
# number of moves required to get from the cell to the goal. 
#
# If a cell is a wall or it is impossible to reach the goal from a cell,

serser

# https://classroom.udacity.com/courses/cs373/lessons/48646841/concepts/486468390923
# -----------
# User Instructions:
#
# Modify the the search function so that it becomes
# an A* search algorithm as defined in the previous
# lectures.
#
# Your function should return the expanded grid
# which shows, for each element, the count when

serser

# -----------
# User Instructions:
#
# Modify the the search function so that it returns
# a shortest path as follows:
# 
# [['>', 'v', ' ', ' ', ' ', ' '],
#  [' ', '>', '>', '>', '>', 'v'],
#  [' ', ' ', ' ', ' ', ' ', 'v'],
#  [' ', ' ', ' ', ' ', ' ', 'v'],