def main():
global env, RL
env = Maze('./env/maps/map3.json', full_observation=True)
RL = DeepQNetwork(
n_actions=4,
n_features=env.height * env.width,
restore_path=None,
# restore_path=base_path + 'model_dqn.ckpt',
learning_rate=0.00001,
reward_decay=0.9,
e_greedy=0.95,
replace_target_iter=4e4,
batch_size=64,
e_greedy_init=0,
# e_greedy_increment=None,
e_greedy_increment=1e-3,
output_graph=False,
)
env.after(100, run_maze)
env.mainloop()
def main():
global env
env = Maze('./env/maps/map3.json')
env.after(100, run_maze)
env.mainloop() # mainloop() to run the application.
# action = rl.choose_action(str(observation))
# observation_, reward, done = env.step(action)
# rl.learn(str(observation), action, reward, str(observation_))
# observation = observation_
# if done:
# break
# on policy
rl = SarsaTable(actions=list(range(env.n_actions)))
for episode in range(100):
observation = env.reset()
action = rl.choose_action(str(observation))
while True:
observation_, reward, done = env.step(action)
action_ = rl.choose_action(str(observation_))
rl.learn(str(observation), action, reward, str(observation_),
action_)
observation = observation_
action = action_
if done:
break
print('game over')
env.destroy()
if __name__ == '__main__':
env = Maze()
env.after(1000, main)
env.mainloop()
def main():
global env, RL, env_model
# if_dyna = True
# env = Maze('./env/maps/map2.json')
# if if_dyna:
# # ---------- Dyna Q ---------- # #
# from brain.dyna_Q import QLearningTable, EnvModel
# RL = QLearningTable(actions=list(range(env.n_actions)))
# env_model = EnvModel(actions=list(range(env.n_actions)))
# env.after(0, update_dyna_q) # Call function update() once after given time/ms.
# else:
# # # -------- Q Learning -------- # #
# from brain.Q_learning import QLearningTable
# RL = QLearningTable(actions=list(range(env.n_actions)))
# env.after(0, update_q()) # Call function update() once after given time/ms.
time_cmp = []
# -------- Q Learning -------- # #
from brain.Q_learning import QLearningTable
start = time.time()
env = Maze('./env/maps/map2.json')
RL = QLearningTable(actions=list(range(env.n_actions)))
env.after(0,
update_q()) # Call function update() once after given time/ms.
env.mainloop()
sum_time = time.time() - start
time_cmp.append(sum_time)
# ---------- Dyna Q ---------- # #
from brain.dyna_Q import QLearningTable, EnvModel
for n in [5, 10, 25, 50]:
start = time.time()
env = Maze('./env/maps/map2.json')
RL = QLearningTable(actions=list(range(env.n_actions)))
env_model = EnvModel(actions=list(range(env.n_actions)))
print('Dyna-{}'.format(n))
env.after(0, update_dyna_q,
n) # n is the parameter of update_dyna_q().
env.mainloop() # mainloop() to run the application.
sum_time = time.time() - start
time_cmp.append(sum_time)
# This part must after env.mainloop()
# plot all lines.
all_aver_steps = [np.load('./logs/q_learning/q_learning.npy').tolist()]
for n in [5, 10, 25, 50]:
all_aver_steps.append(
np.load('./logs/dyna_q/dyna_q_{}.npy'.format(n)).tolist())
plot_multi_lines(
all_aver_steps,
all_labels=['q_learning', 'dyna_5', 'dyna_10', 'dyna_25', 'dyna_50'],
save_path='./logs/cmp_all.png')
# only plot dyna_Q
all_aver_steps = []
for n in [5, 10, 25, 50]:
all_aver_steps.append(
np.load('./logs/dyna_q/dyna_q_{}.npy'.format(n))[0:100].tolist())
plot_multi_lines(all_aver_steps,
all_labels=['dyna_5', 'dyna_10', 'dyna_25', 'dyna_50'],
save_path='./logs/cmp_all_dyna_Q.png')
print(time_cmp)
def main():
global env
env = Maze('./env/maps/map1.json', full_observation=True)
env.after(100, run_maze) # Call function update() once after given time/ms.
env.mainloop() # mainloop() to run the application.
