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/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

# ----------
# 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/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

# 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/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

# -----------
# 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

# -----------
# 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

# https://classroom.udacity.com/courses/cs373/lessons/48721468/concepts/487421890923
# -------------
# User Instructions
#
# Here you will be implementing a cyclic smoothing
# algorithm. This algorithm should not fix the end
# points (as you did in the unit quizzes). You  
# should use the gradient descent equations that
# you used previously.
#

serser

# -------------
# User Instructions
#
# Now you will be incorporating fixed points into
# your smoother. 
#
# You will need to use the equations from gradient
# descent AND the new equations presented in the
# previous lecture to implement smoothing with
# fixed points.