# swap observation
observation = observation_
# break while loop when end of this episode
if done:
break
step += 1
# end of game
print('game over')
if __name__ == "__main__":
# maze game
env = EVs()
RL = DeepQNetwork(
env.n_actions,
env.n_features,
learning_rate=0.01,
reward_decay=0.9,
e_greedy=0.9,
replace_target_iter=200,
memory_size=2000,
# output_graph=True
)
run_EVs()
# env.after(100, run_EVs)
# env.mainloop()
# print(env.actions[100:110])
import gym
from brain import DeepQNetwork
env = gym.make('MountainCar-v0')
env = env.unwrapped
print(env.action_space)
print(env.observation_space)
print(env.observation_space.high)
print(env.observation_space.low)
RL = DeepQNetwork(
n_actions=3,
n_features=2,
learning_rate=0.001,
e_greedy=0.9,
replace_target_iter=300,
memory_size=3000,
e_greedy_increment=0.0002,
)
total_steps = 0
for i_episode in range(10):
observation = env.reset()
ep_r = 0
while True:
env.render()
action = RL.choose_action(observation)
args = parser.parse_args()
if args.method == 'Q-learning':
RL = QLearningTable(range(0, env.action_space.n))
elif args.method == 'Sarsa':
RL = SarsaTable(range(0, env.action_space.n))
elif args.method == 'SarsaLambda':
RL = SarsaLambdaTable(range(0, env.action_space.n))
elif args.method == 'DQN':
if args.test == 'True':
RL = DeepQNetwork(env.action_space.n,
2,
lr=0.1,
batch_size=128,
reward_decay=0.9,
e_greedy=0.9,
replace_target_iter=300,
memory_size=3000,
e_greedy_increment=0.0001,
path='./model/model',
test=True)
else:
RL = DeepQNetwork(
env.action_space.n,
2,
lr=0.1,
batch_size=128,
reward_decay=0.9,
e_greedy=0.9,
replace_target_iter=300,
memory_size=3000,
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--reuse',
default='False',
help='is testing mode or not')
args = parser.parse_args()
RL = DeepQNetwork(
3,
2,
args.reuse,
learning_rate=0.1,
reward_decay=0.9,
e_greedy=0.9,
replace_target_iter=200,
memory_size=2000,
# output_graph=True
)
run()
import matplotlib.pyplot as plt
plt.plot(np.arange(len(steps)), steps)
plt.ylabel('steps cost')
plt.xlabel('episode')
plt.savefig('steps_picture.png')
plt.show()
def run():
step = 0
for episode in range(nEpisodes):
state = game.reset()
while True:
action = RL.chooseAction(state)
observation, reward, done = game.step(action)
RL.storeTransition(state, action, reward, observation)
if step > 200 and step % 5 == 0:
RL.learn()
state = observation
if done:
break
step += 1
print("score : ", game.score, game.board.getState())
print('done')
if __name__ == '__main__':
game = Game()
RL = DeepQNetwork(game.nActions,
game.nFeatures,
learningRate=0.01,
replaceTarget=200,
memorySize=2000)
run()
evaluation()
def evaluation():
"""Evaluate the performance of AI.
"""
pass
if __name__ == '__main__':
# get the DeepQNetwork Agent
RL = DeepQNetwork(
N_ACTIONS,
N_FEATURES,
learning_rate=0.001,
reward_decay=0.9,
e_greedy=0.9,
replace_target_iter=300,
memory_size=2000,
e_greedy_increment=None,
e_policy_threshold=REPLY_START_THRESHOLD,
)
# Calculate running time
start_time = time.time()
run()
end_time = time.time()
running_time = (end_time - start_time) / 60
print('running_time: ' + str(running_time) + 'min')