def main():
# 이미지 저장 경로 확인 및 생성
if not os.path.exists('images/'):
os.makedirs('images/')
# 그리드 월드 환경 객체 생성
env = GridWorld(height=GRID_HEIGHT,
width=GRID_WIDTH,
start_state=(0, 0),
terminal_states=TERMINAL_STATES,
transition_reward=-1.0,
terminal_reward=-1.0,
outward_reward=-1.0)
env.reset()
values, returns = first_visit_mc_prediction(env, 1.0, 10000)
print("First Visit")
for i in range(GRID_HEIGHT):
for j in range(GRID_WIDTH):
print("({0}, {1}): {2:5.2f}".format(i, j, values[i, j]))
print()
draw_grid_world_image(values, 'images/first_visit_mc_state_values.png',
GRID_HEIGHT, GRID_WIDTH)
print()
values, returns = every_visit_mc_prediction(env, 1.0, 10000)
print("Every Visit")
for i in range(GRID_HEIGHT):
for j in range(GRID_WIDTH):
print("({0}, {1}): {2:5.2f}".format(i, j, values[i, j]))
print()
draw_grid_world_image(values, 'images/every_visit_mc_state_values.png',
GRID_HEIGHT, GRID_WIDTH)
def main():
# 이미지 저장 경로 확인 및 생성
if not os.path.exists('images/'):
os.makedirs('images/')
# 그리드 월드 환경 객체 생성
env = GridWorld(
height=GRID_HEIGHT,
width=GRID_WIDTH,
start_state=None, # exploring start
terminal_states=TERMINAL_STATES,
transition_reward=-1.0,
terminal_reward=-1.0,
outward_reward=-1.0)
env.reset()
# n-스텝
n = 3
# 스텝 사이즈
alpha = 0.2
# 가 수행당 1번의 에피소드 수행
episodes = 1000
values = dict()
for i in range(GRID_HEIGHT):
for j in range(GRID_WIDTH):
values[(i, j)] = 0.0
for ep in range(episodes):
temporal_difference(env, values, n, alpha)
draw_grid_world_image(values, 'images/grid_world_fixed_params.png',
GRID_HEIGHT, GRID_WIDTH)
def make_environment(environment_params):
if environment_params['env_name'] == 'gridworld':
from environments.gridworld import GridWorld
env = GridWorld(environment_params['grid_size'])
else:
env = Environment(
environment_params['env_name'],
grid_size=environment_params['grid_size'],
last_n=environment_params['last_n'],
delta_preprocessing=environment_params['delta_preprocessing'])
return env
def main():
# 그리드 월드 환경 객체 생성
env = GridWorld(
height=GRID_HEIGHT,
width=GRID_WIDTH,
start_state=None, # exploring start
terminal_states=TERMINAL_STATES,
transition_reward=-1.0,
terminal_reward=-1.0,
outward_reward=-1.0)
runs = 10
step_n = [1, 2, 4, 8, 16]
data = np.zeros(shape=(len(step_n), MAX_EPISODES))
for run in range(runs):
print("RUNS: {0}".format(run))
for idx_n, n in enumerate(step_n):
Q = state_action_value(env)
policy = generate_e_greedy_policy(env, EPSILON, Q)
print("n={0} ".format(n), end=" ")
_, episode_reward_list = n_step_sarsa(env, Q, policy, n)
avg_episode_reward_list = []
for episode in range(MAX_EPISODES):
avg_episode_reward_list.append(
episode_reward_list[max(0, episode - 10):(episode +
1)].mean())
for idx in range(MAX_EPISODES):
data[idx_n, idx] += avg_episode_reward_list[idx]
print()
data[:, :] /= runs
marker = ['o', 'x', '.', 's', '*', '+', '|', '^', 'D', ' ']
for idx_n, n in enumerate(step_n):
plt.plot(range(0, MAX_EPISODES, 5),
data[idx_n, ::5],
marker=marker[idx_n],
label='n = {0}'.format(step_n[idx_n]))
plt.xlabel('에피소드')
plt.ylabel('에피소드별 평균 리워드')
plt.legend()
plt.savefig('images/n_step_sarsa.png')
plt.close()
def main():
# 그리드 월드 환경 객체 생성
env = GridWorld(
height=GRID_HEIGHT,
width=GRID_WIDTH,
start_state=None, # exploring start
terminal_states=TERMINAL_STATES,
transition_reward=-1.0,
terminal_reward=-1.0,
outward_reward=-1.0)
env.reset()
MC = OffPolicyMonteCarloControl(env)
MC.off_policy_control()
print_grid_world_policy(env, MC.target_policy)
def main():
# 그리드 월드 환경 객체 생성
env = GridWorld(
height=GRID_HEIGHT,
width=GRID_WIDTH,
start_state=None, # exploring start
terminal_states=TERMINAL_STATES,
transition_reward=-1.0,
terminal_reward=-1.0,
outward_reward=-1.0)
Q = state_action_value(env)
policy = generate_e_greedy_policy(env, EPSILON, Q)
n_step_sarsa(env, Q, policy, 4)
# print policy
print_grid_world_policy(env, policy)
def main():
# 그리드 월드 환경 객체 생성
env = GridWorld(
height=GRID_HEIGHT,
width=GRID_WIDTH,
start_state=None, # exploring start
terminal_states=TERMINAL_STATES,
transition_reward=-1.0,
terminal_reward=-1.0,
outward_reward=-1.0)
MC = MonteCarloControl(env)
MC.exploring_start_control()
with np.printoptions(precision=2, suppress=True):
for i in range(GRID_HEIGHT):
for j in range(GRID_WIDTH):
print(i, j, ": UP, DOWN, LEFT, RIGHT", MC.policy[(i, j)][1])
print()
def grid_world_policy_evaluation():
# 그리드 월드 환경 객체 생성
env = GridWorld(height=GRID_HEIGHT,
width=GRID_WIDTH,
start_state=(0, 0),
terminal_states=TERMINAL_STATES,
transition_reward=-1.0,
terminal_reward=-1.0,
outward_reward=-1.0)
env.reset()
# 수렴 시킨 상태 가치를 이미지로 저장하고 반복 횟수 반환 받음
state_values, iteration = compute_state_value(env)
print('정책 평가 --> 상태 가치 수렴: {} 회 반복'.format(iteration))
print(state_values)
draw_grid_world_image(np.round(state_values, decimals=2),
'images/state_values.png', GRID_HEIGHT, GRID_WIDTH)
def main():
# 그리드 월드 환경 객체 생성
env = GridWorld(
height=GRID_HEIGHT,
width=GRID_WIDTH,
start_state=None, # exploring start
terminal_states=TERMINAL_STATES,
transition_reward=-1.0,
terminal_reward=-1.0,
outward_reward=-1.0)
Q = state_action_value(env)
# EPSILON-Greedy 정책 생성
policy = generate_e_greedy_policy(env, EPSILON, Q)
# 무작위 정책 생성
b = generate_random_policy(env)
policy, episode_reward_list = n_step_off_policy_td(env, Q, policy, b, 2)
# print policy
print_grid_world_policy(env, policy)
def main():
# 그리드 월드 환경 객체 생성
env = GridWorld(
height=GRID_HEIGHT,
width=GRID_WIDTH,
start_state=None, # exploring start
terminal_states=TERMINAL_STATES,
transition_reward=-1.0,
terminal_reward=-1.0,
outward_reward=-1.0)
env.reset()
MC = SoftPolicyMonteCarloControl(env)
MC.soft_policy_control()
with np.printoptions(precision=2, suppress=True):
for i in range(GRID_HEIGHT):
for j in range(GRID_WIDTH):
print(
i, j, ": UP, DOWN, LEFT, RIGHT", MC.policy[(i, j)][1],
env.action_space.ACTION_SYMBOLS[np.argmax(
MC.target_policy[(i, j)][1])])
print()
def main():
# 그리드 월드 환경 객체 생성
env = GridWorld(height=GRID_HEIGHT,
width=GRID_WIDTH,
start_state=(0, 0),
terminal_states=TERMINAL_STATES,
transition_reward=-1.0,
terminal_reward=-1.0,
outward_reward=-1.0)
env.reset()
state_action_values, returns = first_visit_mc_prediction(env, 1.0, 10000)
print("First Visit")
for i in range(GRID_HEIGHT):
for j in range(GRID_WIDTH):
print("({0}, {1}):".format(i, j))
for action in range(NUM_ACTIONS):
print(" Action {0}: {1:5.2f}".format(
env.action_space.ACTION_SYMBOLS[action],
state_action_values[((i, j), action)]))
print()
print()
state_action_values, returns = every_visit_mc_prediction(env, 1.0, 10000)
print("Every Visit")
for i in range(GRID_HEIGHT):
for j in range(GRID_WIDTH):
print("({0}, {1}):".format(i, j))
for action in range(NUM_ACTIONS):
print(" Action {0}: {1:5.2f}".format(
env.action_space.ACTION_SYMBOLS[action],
state_action_values[((i, j), action)]))
print()
print()
def main():
# 그리드 월드 환경 객체 생성
env = GridWorld(height=GRID_HEIGHT,
width=GRID_WIDTH,
start_state=(0, 0),
terminal_states=TERMINAL_STATES,
transition_reward=-1.0,
terminal_reward=-1.0,
outward_reward=-1.0)
env.reset()
MC = OffPolicyMonteCarloPrediction(env)
MC.off_policy_prediction()
for i in range(GRID_HEIGHT):
for j in range(GRID_WIDTH):
print("({0}, {1}):".format(i, j))
for action in range(NUM_ACTIONS):
print(" Action {0}: {1:5.2f}".format(
env.action_space.ACTION_SYMBOLS[action],
MC.state_action_values[((i, j), action)]))
print()
print()
iter_num_policy_evaluation = self.value_evaluation()
print("*** 가치 평가 [수렴까지 누적 반복 횟수: {0}] ***".format(iter_num_policy_evaluation))
self.policy_setup()
print("*** 정책 셋업 완료 ***")
return self.state_values, self.policy
if __name__ == '__main__':
# 그리드 월드 환경 객체 생성
env = GridWorld(
height=GRID_HEIGHT,
width=GRID_WIDTH,
start_state=(0, 0),
terminal_states=TERMINAL_STATES,
transition_reward=-1.0,
terminal_reward=-1.0,
outward_reward=-1.0
)
env.reset()
VI = ValueIteration(env)
VI.start_iteration()
print(VI.state_values, end="\n\n")
for i in range(GRID_HEIGHT):
for j in range(GRID_WIDTH):
print(i, j, VI.policy[i][j]) # UP, DOWN, LEFT, RIGHT
print()
# MAIN
if __name__ == '__main__':
if not os.path.exists('images/'):
os.makedirs('images/')
value_function = np.zeros((GRID_HEIGHT, GRID_WIDTH))
draw_grid_world_image(np.round(value_function, decimals=0), 'images/empty_grid_world.png', GRID_HEIGHT, GRID_WIDTH)
# 5x5 맵 생성
env = GridWorld(
height=GRID_HEIGHT,
width=GRID_WIDTH,
start_state=(0, 0),
terminal_states=[],
transition_reward=0,
outward_reward=-1.0,
warm_hole_states=[(A_POSITION, A_PRIME_POSITION, 10.0), (B_POSITION, B_PRIME_POSITION, 5.0)]
)
env.reset()
state_values = grid_world_state_values(env)
print(state_values)
draw_grid_world_image(
np.round(state_values, decimals=2), 'images/grid_world_state_values.png', GRID_HEIGHT, GRID_WIDTH
)
print()
env.reset()
marker = '+'
color = 'green'
plt.plot(step_sizes,
performace[method, :],
linestyle=linestyle,
color=color,
marker=marker,
label=label)
plt.xlabel('스텝 사이즈 (alpha)')
plt.ylabel('에피소드 당 누적 보상')
plt.legend()
plt.savefig('images/cumulative_rewards_for_step_size.png')
plt.close()
if __name__ == '__main__':
env = GridWorld(height=GRID_HEIGHT,
width=GRID_WIDTH,
start_state=START_STATE,
terminal_states=TERMINAL_STATES,
transition_reward=-1.0,
terminal_reward=-1.0,
outward_reward=-1.0,
warm_hole_states=[(s, START_STATE, -100.0)
for s in CLIFF_STATES])
cumulative_rewards_for_episodes(env)
cumulative_rewards_for_step_size(env)
from environments.gridworld import GridWorld
from agents.agent import Agent
from controls.control import Sarsa, QLearning, ExpectedSarsa
from utils.train import Train
from utils.comparator import Comparator
from policies.action_value import TabularActionValue
# Train an agent in a episodic gridworld with windy tiles using Sarsa, QLearning
# and ExpectedSarsa control algorithms
trainings = []
controls = [Sarsa(), QLearning(), ExpectedSarsa()]
for control in controls:
game = GridWorld(level=1)
action_value = TabularActionValue(game.get_states(), game.get_actions())
agent = Agent(game, action_value)
train = Train(agent, game, control)
train.train()
trainings.append(train)
# Compare the episodic rewards obtained by the agent during training for the
# different control algorithms
comp = Comparator(*trainings, smoothing=30)
comp.compare_rewards()
from environments.gridworld import GridWorld, State
from lonr.local_no_regret import LocalNoRegret
grid = GridWorld()
lonr = LocalNoRegret(time_limit=15000,
num_agents=1,
state_space=grid.state_space,
actions=grid.actions,
next_states=grid.next_states,
transitions=grid.transitions,
rewards=grid.rewards,
gamma=0.99)
lonr.lonr_v()
import os
print(os.getcwd())
# Set parameters
n_rows = 10
n_cols = 10
discount_rate = 0.4
random_shift = 0.3
goal_state_index = [12, 45]
n_steps = 10
n_demos = 100
# Initialise framework
env = GridWorld(n_rows,
n_cols,
discount_rate,
random_shift=0,
goal_state_index=goal_state_index)
mdp_solver = LinearMDP(env)
# Solve for optimal and get demonstrations
demonstrations = run_optimal(env,
n_rows,
n_cols,
n_steps,
n_demos,
mdp_solver,
show=False)
algo = DeepIRL(env, mdp_solver)
state_values, q_values, policy, log_policy, rewards_vector = algo.run(