def main():
parser = argparse.ArgumentParser()
parser.add_argument('--grid_size', nargs='?', const=1, type=int, default=8)
parser.add_argument('--macro_block', nargs='?', const=1, type=int, default=2)
parser.add_argument('--gamma', nargs='?', const=1, type=float, default=0.90)
args = parser.parse_args()
start_time = time.time()
env = GridWorld(args.grid_size, args.macro_block, args.gamma)
env_time = time.time() - start_time
print ("Time to create environment: {}".format(env_time))
start_time = time.time()
policy, v = value_iteration_sparse(env)
value_iteration_time = time.time() - start_time
print ("Time to find optimal policy: {}".format(value_iteration_time))
start_time = time.time()
weights_inv, policy_inv = irl(env, policy)
irl_time = time.time() - start_time
print ("Time to solve IRL problem: {}".format(irl_time))
print ("Displaying comparison between optimal policy and IRL policy:")
env.draw(policy, policy_inv, weights_inv)
def Q_learning(env: GridWorld,
epsilon: float,
lr: float,
initQ: np.ndarray,
converge=False) -> (np.ndarray, float):
"""
Performs Q learning for single episode in environment and returns learned policy.
:param env: GridWorld subclass
:param epsilon: Exploitation rate
:param lr: learning rate
:param initQ: Q table to update
:param converge: Flag to determine if delta of Q-values need to be tracked
for convergence within set bound.
:return: Updated Q table after a single episode of training and maximum change for any Q-value
"""
# keep track of maximum state-action value change
delta = 0.0
state = env.reset()
done = False
while not done:
# explore action space
if np.random.uniform(0, 1) > epsilon:
action = env.sample()
# exploit
else:
action = np.argmax(initQ[state])
# take step in env
obs, r, done = env.step(action)
# update Q table
prev_value = initQ[state, action]
new_value = prev_value + lr * (r + env.gamma * np.max(initQ[obs]) -
prev_value)
initQ[state, action] = new_value
# update state
state = obs
if converge:
delta = max(delta, np.abs(new_value - prev_value))
return initQ, delta
def control(self):
map_names = ['map{}'.format(i) for i in range(1, 21)]
train_combos = list(product(range(10), range(10)))
test_combos = train_combos
env = PORGBEnv(
ComboEnv(
GridWorld(map_names,
num_obj_types=5,
train_combos=train_combos,
test_combos=test_combos,
window=1,
seed=0)))
control(env)
env.render()
def testNeighbors(self):
"""
#Check that 1, 5, and 9 have the correct neighbors
B B B B B
1 2 3 B 1 2 3 B
4 5 6 => B 4 5 6 B
7 8 9 B 7 8 9 B
B B B B B
"""
gridworld = GridWorld()
#Check 1, index is 0
grid_1 = gridworld.grids[gridworld.map_states(0)]
grid_1_neighbors = list(grid_1.neighbors.values())
grid_1_neighbors_ids = [grid.id for grid in grid_1_neighbors]
results = list(set(grid_1_neighbors_ids) - set([None]))
results.sort()
target = [2,4]
self.assertEqual(results,target)
#Check 5, index is 4
grid_5 = gridworld.grids[gridworld.map_states(4)]
grid_5_neighbors = list(grid_5.neighbors.values())
grid_5_neighbors_ids = [grid.id for grid in grid_5_neighbors]
results = list(set(grid_5_neighbors_ids) - set([None]))
results.sort()
target = [2,4,6,8]
self.assertEqual(results,target)
#Check 9, index is 8
grid_9 = gridworld.grids[gridworld.map_states(8)]
grid_9_neighbors = list(grid_9.neighbors.values())
grid_9_neighbors_ids = [grid.id for grid in grid_9_neighbors]
results = list(set(grid_9_neighbors_ids) - set([None]))
results.sort()
target = [6,8]
self.assertEqual(results,target)
def main(arguments):
parser = create_argparser({
"alpha": {
"default": 0.1
},
"--use_ep_func": {
"dest": "use_ep_func",
"action": "store_true",
"default": True
}
})
args = parser.parse_args(arguments)
grid_world = GridWorld(default_grid, args.p1, args.p2)
default_args = {"epsilon": 0.1, "discount_factor": 0.9}
for arg in default_args:
if arg not in args:
setattr(args, arg, default_args[arg])
run_dict = {}
num_episodes = args.num_episodes
globals()['args'] = args
num_runs = 3 if args.AVERAGE_RUNS else 1
for i in range(num_runs):
start_time = time.time()
q_s_a, q_s_a2 = initialize(grid_world)
if not args.use_ep_func:
_, _, ep_length_log, time_log, avg_ep_length_log, avg_time_log = double_q(
grid_world,
q_s_a,
q_s_a2,
args.epsilon,
num_episodes=num_episodes)
else:
_, _, ep_length_log, time_log, avg_ep_length_log, avg_time_log = double_q(
grid_world,
q_s_a,
q_s_a2,
epsilon_func,
num_episodes=num_episodes)
total_time = time.time() - start_time
run_dict[i] = {
"Episode Length": ep_length_log,
"Time Per Episode": time_log,
"Total Time": total_time,
"Average Time Log": avg_time_log,
"Average Ep Length": avg_ep_length_log
}
print("\nTook {}s to finish {} episodes".format(
total_time, num_episodes))
average_ep_lengths = np.average(np.array(
[run_dict[key]["Episode Length"] for key in run_dict]),
axis=0)
average_ep_time = np.average(np.array(
[run_dict[key]["Time Per Episode"] for key in run_dict]),
axis=0)
average_time = np.average(np.array(
[run_dict[key]["Total Time"] for key in run_dict]),
axis=0)
average_avg_time_log = np.average(np.array(
[run_dict[key]["Average Time Log"] for key in run_dict]),
axis=0)
average_avg_ep_length = np.average(np.array(
[run_dict[key]["Average Ep Length"] for key in run_dict]),
axis=0)
output_deterministic_policy(q_s_a, q_s_a2, grid_world)
return average_ep_lengths, average_ep_time, average_time, average_avg_time_log, average_avg_ep_length
def main():
grid_size = 10
grid_world = GridWorld(grid_size,
num_obstacles=20,
stochastic_cell_ratio=0.1)
params = {}
params['type'] = 'value_iteration'
params['grid_size'] = grid_size
params['rewards'] = grid_world.rewards
params['transition_matrix'] = grid_world.transition_matrix
params['step_func'] = GridWorld.deterministic_step
params['discount'] = 0.9
agent = AgentFactory.create_agent(params)
episode_ended = False
while True:
grid_world.get_user_input()
grid_world.draw_with_state_values(
agent.v, policy=agent.pi if grid_world.render_policy else None)
if not grid_world.pause:
if episode_ended:
grid_world.restart_episode()
grid_world.draw_black_screen()
episode_ended = False
else:
agent.do_job()
if agent.ready_to_play():
action = agent.get_action(grid_world.pos)
episode_ended, _, _ = grid_world.step(action)
grid_world.tick_tock()
from helper import create_argparser
from env import GridWorld
import random
import math
from copy import copy
import time
#Creating command line parser
parser = create_argparser()
args = parser.parse_args()
grid_world = GridWorld(args.p1,
args.p2,
args.r_up,
args.r_down,
args.r_left,
args.r_right,
grid_world_size=4,
starting_state=8)
'''
Policy Evaluation
'''
def policy_evaluation(v_s, pi_s, grid_world):
#Policy evaluation implementation
delta = math.inf
while delta > args.theta:
delta = 0
for i, s in enumerate(v_s):
if i == grid_world.terminal_state:
print(row)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--gamma", type=float, default=1.0)
parser.add_argument("--alpha", type=float, default=0.0001)
parser.add_argument("--map_size", type=int, default=4)
parser.add_argument("--num_ep", type=int, default=50000)
parser.add_argument("--method", type=to_str, default='mc')
args = parser.parse_args()
# Set hyper-parameters
gamma = args.gamma #
alpha = args.alpha
map_size = args.map_size
num_ep = args.num_ep # 에피소드 진행 횟수
method = args.method
env = GridWorld()
agent = Agent()
data = np.zeros((map_size, map_size))
if method == 'mc':
mc(data, gamma, alpha, num_ep)
elif method == 'td':
print(data, gamma, alpha, num_ep, method)
td(data, gamma, alpha, num_ep)
def main(arguments):
parser = create_argparser()
args = parser.parse_args(arguments)
grid_world = GridWorld(default_grid, args.p1, args.p2)
default_args = {"epsilon": 0.1, "discount_factor": 0.9}
#For nice syntax
for arg in default_args:
if arg not in args:
setattr(args, arg, default_args[arg])
num_episodes = args.num_episodes
run_dict = {}
#injecting into global scope
globals()['args'] = args
num_runs = 3 if args.AVERAGE_RUNS else 1
for i in range(num_runs):
start_time = time.time()
pi, q_s_a, returns = initialize(grid_world)
_, _, ep_length_log, time_log, avg_ep_length_log, avg_time_log = gpi(
grid_world, pi, q_s_a, returns, num_episodes=num_episodes)
total_time = time.time() - start_time
run_dict[i] = {
"Episode Length": ep_length_log,
"Time Per Episode": time_log,
"Total Time": total_time,
"Average Time Log": avg_time_log,
"Average Ep Length": avg_ep_length_log
}
print("\nTook {}s to finish {} episodes".format(
total_time, num_episodes))
average_ep_lengths = np.average(np.array(
[run_dict[key]["Episode Length"] for key in run_dict]),
axis=0)
average_ep_time = np.average(np.array(
[run_dict[key]["Time Per Episode"] for key in run_dict]),
axis=0)
average_time = np.average(np.array(
[run_dict[key]["Total Time"] for key in run_dict]),
axis=0)
average_avg_time_log = np.average(np.array(
[run_dict[key]["Average Time Log"] for key in run_dict]),
axis=0)
average_avg_ep_length = np.average(np.array(
[run_dict[key]["Average Ep Length"] for key in run_dict]),
axis=0)
res = [
average_ep_lengths, average_ep_time, average_time,
average_avg_time_log, average_avg_ep_length
]
graph_names = [
"Episode Length", "Time Per Episode", "Total Time in Seconds",
"Time Per Episode (Moving Average 10 ep)",
"Episode Length (Moving Average 10 ep)"
]
y_axis_names = [
"Episode Length in Steps", "Time Per Episode in Seconds",
"Total Time in Seconds", "Time Per Episode in Seconds",
"Episode Length in Steps"
]
#outputting policy
output_deterministic_policy(pi, grid_world)
for i in [0, 1]:
t = np.linspace(1, num_episodes, num=num_episodes)[0::10]
plt.plot(t, res[i][0::10], label="mc")
plt.title(graph_names[i])
plt.xlabel("Episode Number")
plt.ylabel(y_axis_names[i])
plt.legend()
plt.savefig(graph_names[i] + "_mc" + ".jpg")
plt.close()
for i in [-2, -1]:
t = np.linspace(1, num_episodes, num=num_episodes / 10)
plt.plot(t, res[i], label="mc")
plt.title(graph_names[i])
plt.xlabel("Episode Number")
plt.ylabel(y_axis_names[i])
plt.legend()
plt.savefig(graph_names[i] + "_mc" + ".jpg")
plt.close()
return average_ep_lengths, average_ep_time, average_time, average_avg_time_log, average_avg_ep_length
def testCreate(self):
gridworld = GridWorld()
results = gridworld.get_pretty()
target = "bbbbb\nbaaab\nbaaab\nbaaab\nbbbbb\n"
self.assertEqual(results,target)
def testMap_states(self):
gridworld = GridWorld(size= (3,4))
start = [0,1,2,3,4,5,6,7,8,9,10,11]
target = [6,7,8,11,12,13,16,17,18,21,22,23]
results = list(map(gridworld.map_states, start))
self.assertEqual(results,target)