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mixture_policy.py
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'''Mixture policy approach baseline implementation: https://core.ac.uk/download/pdf/212996663.pdf'''
import numpy as np
import argparse
from Fair_Taxi_MDP_Penalty_V2 import Fair_Taxi_MDP_Penalty_V2
from linear_scalarization import scalarized_ql
import time
def run_Q_learning(episodes, alpha, epsilon, gamma, init_val, objective):
"""
Q Learning to optimize ONE objective
params: objective (int) index of the objective to maximize from
"""
Q_table = np.zeros([env.observation_space.n, env.action_space.n])
Q_table = Q_table + init_val
r_acc = np.zeros([len(env.loc_coords)])
for i in range(1, episodes+1):
state = env.reset()
done = False
while not done:
if np.random.uniform(0, 1) < epsilon: action = env.action_space.sample()
else: action = np.argmax(Q_table[state])
next, reward, done = env.step(action)
Q_table[state, action] = (1 - alpha)*Q_table[state, action] + alpha*(reward[objective]+gamma*np.max(Q_table[next]))
state = next
r_acc += reward
print('Episode {}: {}'.format(i, r_acc))
return Q_table
def greedy(vec, weights):
'''Helper function'''
arr = []
for val in vec: arr.append(np.dot(weights, val)) # linear scalarization
return np.argmax(arr)
def mixture(episodes, timesteps, alpha, epsilon, gamma, init_val, dimension, weights_arr, interval, run=1, save=True, table=None):
'''Implementation of mixture policy algorithm'''
dims = [i for i in range(len(weights_arr))]
if type(table) == type(None):
policies = []
for dim in dims: # Obtain set of policies
q = np.full([env.observation_space.n, env.action_space.n, dimension], init_val, dtype=float)
policies.append(q)
else:
policies = table
nsw_data, total_data = [], []
for i in range(1, episodes+1):
R_acc = np.zeros(dimension)
state = env.reset()
done = False
count, dim, c = 0, 0, 0
Q = policies[dim]
weights = weights_arr[dim]
while not done:
if count > int(timesteps/dimension/interval): # determines the period of changing policies
dim += 1
if dim >= dimension: dim = 0 # back to first objective after a "cycle"
Q = policies[dim]
weights = weights_arr[dim]
count = 0 # change policy after t/d timesteps
if np.random.uniform(0, 1) < epsilon: action = env.action_space.sample()
else: action = greedy(Q[state], weights)
next, reward, done = env.step(action)
count += 1
next_action = greedy(Q[next], weights)
for j in range(len(Q[state, action])):
Q[state,action][j] = Q[state,action][j] + alpha*(reward[j]+gamma*Q[next,next_action][j]-Q[state,action][j])
state = next
R_acc += np.power(gamma, c)*reward
c += 1
R_acc = np.where(R_acc < 0, 0, R_acc) # Replace the negatives with 0
nsw_score = np.power(np.product(R_acc), 1/len(R_acc))
nsw_data.append(nsw_score)
total_data.append(np.sum(R_acc))
print('Episode {}\nAccumulated Discounted Reward: {}\nNSW: {}\n'.format(i, R_acc, nsw_score))
if save == True:
np.save('taxi_q_tables_V2/mixture_size{}_locs{}_run{}_nsw'.format(env.size, len(env.loc_coords), run), nsw_data)
np.save('taxi_q_tables_V2/mixture_size{}_locs{}_run{}_total'.format(env.size, len(env.loc_coords), run), total_data)
return nsw_data, total_data, np.mean(nsw_data), policies
def nsw(vec, nsw_lambda):
'''Helper function'''
vec = vec + nsw_lambda
vec = np.where(vec <= 0, nsw_lambda, vec) # replace any negative values or zeroes with lambda
return np.sum(np.log(vec)) # numpy uses natural log
def get_setting(size, num_locs):
"""
To store environment settings
Parameters
----------
size : int
size of the grid world in N x N
num_locs : int
number of location destination pairs
"""
if num_locs == 2:
loc_coords = [[0,0],[3,2]]
dest_coords = [[0,4],[3,3]]
elif num_locs == 3:
loc_coords = [[0,0],[0,5],[3,2]]
dest_coords = [[0,4],[5,0],[3,3]]
elif num_locs == 4:
loc_coords = [[0,0], [0,5], [3,2], [9,0]]
dest_coords = [[0,4], [5,0], [3,3], [0,9]]
elif num_locs == 5:
loc_coords = [[0,0],[0,5],[3,2],[9,0],[4,7]]
dest_coords = [[0,4],[5,0],[3,3],[0,9],[8,9]]
else:
loc_coords = [[0,0],[0,5],[3,2],[9,0],[8,9],[6,7]]
dest_coords = [[0,4],[5,0],[3,3],[0,9],[4,7],[8,3]]
return size, loc_coords, dest_coords
def grid_search(episodes, timesteps, alpha, epsilon, gamma, init_val, dimension, weights_arr, intervals):
# search for the optimal interval of switching policies
arr = []
for val in intervals:
start_time = time.time()
avg_nsw = mixture(episodes, timesteps, alpha, epsilon, gamma, init_val, dimension, weights_arr, val, save=False)
print('Average NSW{}\n Time Taken: {}\n'.format(avg_nsw, start_time-time.time()))
arr.append(avg_nsw)
np.save('mixutre_best_interval', intervals[np.argmax(arr)])
np.save('mixture_all_interval', arr)
if __name__ == '__main__':
prs = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="""Mixture Policy on Taxi""")
prs.add_argument("-f", dest="fuel", type=int, default=10000, required=False, help="Timesteps each trajectory\n")
prs.add_argument("-ep", dest="episodes", type=int, default=5000, required=False, help="episodes.\n")
prs.add_argument("-a", dest="alpha", type=float, default=0.1, required=False, help="Alpha learning rate.\n")
prs.add_argument("-e", dest="epsilon", type=float, default=0.1, required=False, help="Exploration rate.\n")
prs.add_argument("-g", dest="gamma", type=float, default=0.999, required=False, help="Discount rate\n")
prs.add_argument("-i", dest="init_val", type=int, default=30, required=False, help="Initial values\n")
prs.add_argument("-gs", dest="size", type=int, default=10, required=False, help="Grid size\n")
prs.add_argument("-d", dest="dimension", type=int, default=2, required=False, help="Dimension of reward\n")
args = prs.parse_args()
size, loc_coords, dest_coords = get_setting(args.size, args.dimension)
fuel = args.fuel
env = Fair_Taxi_MDP_Penalty_V2(size, loc_coords, dest_coords, fuel, output_path='')
env.seed(1122)
arr = np.arange(2,102,2)
arr2 = np.arange(200,1100,100)
arr_3 = np.concatenate((arr,arr2))
grid_search(500, args.fuel, args.alpha, args.epsilon,
args.gamma, args.init_val, args.dimension, [[0.21, 0.79],[1.0, 0.0]], arr_3)