def run():
env = gym.make('CartPole-v0')
env = env.unwrapped
N_ACTIONS = env.action_space.n
N_STATES = env.observation_space.shape[0]
RL = DeepQNetwork(N_ACTIONS, N_STATES)
step = 0
for i in range(600): # 玩300个回合
# init env
observation = env.reset()
step_in = 0
while True:
# refresh env
env.render()
action = RL.choose_action(observation)
observation_, reward, done, info = env.step(action)
# modify the reward
x, x_dot, theta, theta_dot = observation_
r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8
r2 = (env.theta_threshold_radians -
abs(theta)) / env.theta_threshold_radians - 0.5
r = r1 + r2
RL.store_transition(observation, action, r, observation_)
if step > 200 and step % 5 == 0:
RL.learn()
if done:
print('step_in:%s reward:%s' % (step_in, reward))
plot_data.append(step_in)
break
observation = observation_
step += 1
step_in += 1
# end of game
print('game over')
# env.destroy()
# plot_data = np.array(plot_data, dtype='float32')
# plot_data = np.divide(plot_data, plot_data.max())
print(plot_data)
e_greedy=0.1,
replace_target_iter=200,
memory_size=2000)
episodes = 2000
step = 0
for i in range(episodes):
state = env.reset()
while True:
env.render()
feature = [0] * len(env.getStates())
feature[state - 1] = 1
feature = np.hstack(feature)
action = RL.choose_action(feature)
state_, reward, done = env.step(action)
feature_ = [0] * len(env.getStates())
feature_[state_ - 1] = 1
feature_ = np.hstack(feature_)
RL.store_transition(feature, action, reward, feature_)
if (step > 200) and (step % 5 == 0):
RL.learn()
state = state_
if done:
learning_rate=0.01, e_greedy=0.9,
replace_target_iter=100, memory_size=1000,
)
total_steps = 0
reward_c = []
show = []
running_reward = 0
for i_episode in range(1000):
t = 0
observation = env.reset()
ep_r = 0
while True:
# env.render()
action = RL.choose_action(observation)
observation_, reward, done, info = env.step(action)
# the smaller theta and closer to center the better
# x, x_dot, theta, theta_dot = observation_
# r1 = (env.x_threshold - abs(x))/env.x_threshold - 0.8
# r2 = (env.theta_threshold_radians - abs(theta))/env.theta_threshold_radians - 0.5
# reward = r1 + r2
if done:
reward = -1
RL.store_transition(observation, action, reward, observation_)
ep_r += reward
if total_steps > 1000:
reward_decay=0.9,
e_greedy=0.9,
replace_target_iter=200,
memory_size=1048576,
batch_size=50 * 700,
training=True,
import_file='saved/trained_dqn')
step = 0
score_history = []
for episode in range(600):
blue_state, red_state = env.reset()
score = 0
#Main game loop
while True:
blue_action = RL.choose_action(blue_state)
red_action = 0 #RL.choose_action(red_state)
blue_state_, red_state, blue_reward, done = env.step(
translate_int_action(blue_action),
translate_int_action(red_action))
RL.store_transition(blue_state, blue_action, blue_reward, blue_state_)
if (step > 200) and (step % 50 == 0):
RL.learn()
blue_state = blue_state_
env.render()