def main(grid_size, discount_factor, epochs, learning_rate):
# Creates an object for using the RobotMarkovModel class
robot_mdp = RobotMarkovModel()
# Reads the trajectory data from the csv files provided and returns the trajectories
# The trajectories is numpy array of the number of trajectories provided
trajectories = robot_mdp.generate_trajectories()
# Finds the length of the trajectories data
n_trajectories = len(trajectories)
# Initialize the IRL class object, provide trajectory length as input, currently its value is 3
irl = MaxEntIRL(n_trajectories, grid_size)
# For storing results
filename = "/home/vignesh/PycharmProjects/dvrk_automated_suturing/data/trial4_grid11_parallel/weights_grid11.txt"
# Calculates the reward function based on the Max Entropy IRL algorithm
reward, weights = irl.max_ent_irl(trajectories, discount_factor,
n_trajectories, epochs, learning_rate,
filename)
print "r is ", reward.reshape((grid_size, grid_size), order='F')
# print "r shape ", reward.shape
print "weights is ", weights.reshape((grid_size, grid_size), order='F')
# print "policy is ", policy[0][0]
file_open = open(filename, 'a')
file_open.write("\n \n \n \n Final result \n")
savetxt(filename, weights, delimiter=',', fmt="%10.5f", newline=", ")
file_open.close()
def main(discount, epochs, learning_rate, weights, trajectory_length,
n_policy_iter):
# Creates an object for using the RobotMarkovModel class
robot_mdp = RobotMarkovModel()
# Initialize the IRL class object, provide trajectory length as input, currently its value is 1
irl = MaxEntIRL(trajectory_length)
# trajectory_features_array, trajectory_rewards_array = robot_mdp.trajectories_features_rewards_array(weights)
# Finds the sum of features of the expert trajectory and list of all the features of the expert trajectory
sum_trajectory_features, complete_features_array = robot_mdp.generate_trajectories(
)
# print "traj array ", trajectory_features_array.shape, trajectory_features_array
# print "complete array ", complete_features_array.shape, complete_features_array
# print trajectory_rewards_array
# print model_rot_par_r
# Returns the state and actions spaces of the expert trajectory
state_trajectory_array, action_trajectory_array = robot_mdp.trajectories_data(
)
# Finds the length of the trajectories data
n_trajectories = len(state_trajectory_array)
# Calculates the reward function based on the Max Entropy IRL algorithm
reward, alpha = irl.max_ent_irl(sum_trajectory_features,
complete_features_array, discount,
n_trajectories, epochs, learning_rate,
n_policy_iter, weights)
print "r is ", reward
print "alpha is ", alpha
def main(grid_size, discount_factor, epochs, learning_rate):
# Creates an object for using the RobotMarkovModel class
robot_mdp = RobotMarkovModel()
# Reads the trajectory data from the csv files provided and returns the trajectories
# The trajectories is numpy array of the number of trajectories provided
trajectories = robot_mdp.generate_trajectories()
# Finds the length of the trajectories data
n_trajectories = len(trajectories)
# Initialize the IRL class object, provide trajectory length as input, currently its value is 3
irl = MaxEntIRL(n_trajectories, grid_size)
# Calculates the reward function based on the Max Entropy IRL algorithm
reward, weights = irl.max_ent_irl(trajectories, discount_factor, n_trajectories, epochs, learning_rate)
print "r is ", reward.reshape((grid_size, grid_size), order='F')
# print "r shape ", reward.shape
print "weights is ", weights.reshape((grid_size, grid_size), order='F')
def main(discount, epochs, learning_rate):
# Creates an object for using the RobotMarkovModel class
robot_mdp = RobotMarkovModel()
# Finds the sum of features of the expert trajectory and list of all the features of the expert trajectory
sum_trajectory_features, feature_array_all_trajectories = robot_mdp.generate_trajectories()
# Finds the length of the trajectories data
n_trajectories = len(feature_array_all_trajectories)
# Initialize the IRL class object, provide trajectory length as input, currently its value is 1
irl = MaxEntIRL(n_trajectories)
# Calculates the reward function based on the Max Entropy IRL algorithm
reward, weights = irl.max_ent_irl(sum_trajectory_features, feature_array_all_trajectories, discount,
n_trajectories, epochs, learning_rate)
print "r is ", reward
# print "r shape ", reward.shape
print "weights is ", weights
return V, policy
if __name__ == '__main__':
# goal = np.array([0.005, 0.055, -0.125])
goal = np.array([0.5, 0.5, 0])
# term_state = np.random.randint(0, grid_size ** 3)]
env_obj = RobotStateUtilsP(11, 0.9, goal)
states = env_obj.create_state_space_model_func()
action = env_obj.create_action_set_func()
print "State space created is ", states
print "actions is ", action
index_val = env_obj.get_state_val_index(goal)
print "index val is ", index_val
mdp_obj = RobotMarkovModel()
trajectories = mdp_obj.generate_trajectories()
index_vals = np.zeros(len(trajectories[0]))
rewards = np.zeros(len(states))
for i in range(len(trajectories[0])):
# print "traj is ", trajectories[0][i]
index_vals[i] = env_obj.get_state_val_index(trajectories[0][i])
for _, ele in enumerate(index_vals):
if ele == index_val:
rewards[int(ele)] = 10
else:
rewards[int(ele)] = 1
env_obj.rewards = rewards
print "rewards is ", rewards
# initialize V(s)
V = {}
for state in env_obj.states: