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)
# -*- coding: utf-8 -*-
'''
Author: winddy
'''
import numpy as np
from grid_mdp import GridEnv
from DQN_modified import DeepQNetwork
env = GridEnv()
RL = DeepQNetwork(len(env.getAction()),
len(env.getStates()),
learning_rate=0.01,
reward_decay=0.9,
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)
# move = line.strip().split(',')
# numbers_move = [int(l) for l in move ]
# # print('aaa')
# # print(origin_chess)
# move_way = trans_action_to_A(origin_chess, numbers_move)
# # print(move_way)
# if line.strip().__contains__(',') == False :
# move = line.strip().split(',')
# numbers_move = [float(l) for l in move ]
# print(numbers_move)
if __name__ == "__main__":
#print(S_test[9,8])
#print(trans_S(S_test))
RL = DeepQNetwork(187, 96,
learning_rate=0.01,
reward_decay=0.9,
e_greedy=0.9,
replace_target_iter=200,
memory_size=2000,
# output_graph=True
)
file_path = os.getcwd() + '\\ajax.txt'
parse_txt(file_path)
RL.plot_cost()
# s = '11.200000000000728'
# print(s.__contains__('.'))
# print(np.loadtxt(file_path))
# print(trans_action_to_A(S_test,action_test))
# print(trans_A_to_action(S_test,trans_action_to_A(S_test,action_test)))
# RL take action and get next observation and reward
observation_, reward, done = env.step(action)
RL.store_transition(observation, action, reward, observation_)
observation = observation_
if done:
print('total: %s, reward: %s' % (step, reward))
break
step += 1
time.sleep(1)
# end of game
print('game over')
env.destroy()
if __name__ == '__main__':
env = Maze()
# RL = QLearningTable(actions=list(range(env.n_actions)))
# RL = SarsaLambda(actions=list(range(env.n_actions)))
RL = DeepQNetwork(n_actions=env.n_actions,
n_features=env.n_features,
output_graph=True)
# 开始可视化环境 env
# env.after(100, update)
# env.after(100, update_sarsa)
env.after(100, update_DQN)
# env.after(100, play_once)
env.mainloop()
RL.plot_cost()
def train_hdqnm(seed, file):
# maze game
MAZE_H=3
MAZE_W=3
hell_coord = [0,2]
door_coord = [2,2]
oval_coord = [1,1]
np.random.seed(seed)
tf.set_random_seed(seed)
# maze game
env = Maze(MAZE_H, MAZE_W, hell_coord, door_coord, oval_coord)
n_goals = 3
max_episode = 10000
controller_start = 200
meta_controller_start = 10000
controller = DeepQNetwork(env.n_actions, env.n_features + 1, 'controller',
optimizer='rmsprop',
momentum=0.9,
learning_rate=1e-3,
opt_decay=0.99,
reward_decay=0.99,
e_greedy=0,
e_greedy_max=0.99,
e_greedy_increment=1e-4,
e_greedy_iter=5e3,
replace_target_iter=200,
memory_size=5000,
output_graph=False,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=1e5,
prioritized_replay_eps=1e-6
)
meta_controller = DeepQNetwork(n_goals, env.n_features, 'meta_controller',
optimizer='rmsprop',
momentum=0.9,
learning_rate=1e-3,
opt_decay=0.99,
reward_decay=0.99,
e_greedy=0,
e_greedy_max=0.99,
e_greedy_increment=1e-4,
e_greedy_iter=1e3,
replace_target_iter=200,
memory_size=500,
output_graph=False,
prioritized_replay=True,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=1e3,
prioritized_replay_eps=1e-6,
)
def play_maze():
def goal_reached(g, s):
return ((s[2 * g + 1] == 0) and (s[2 * g + 2] == 0))
s = env.reset()
g = meta_controller.choose_action(s, test=True)
score = 0
while True:
env.render()
a = controller.choose_action(np.hstack((s, g)), test=True)
s_, r, done = env.step(a)
score += r
if done:
break
s = s_
if goal_reached(g, s):
g = meta_controller.choose_action(s, test=True)
return score
def run_maze(max_episode):
def goal_reached(g, s):
return ((s[2 * g + 1] == 0) and (s[2 * g + 2] == 0))
def goal_distance(g, s):
return np.sqrt(s[2 * g + 1] ** 2 + s[2 * g + 2] ** 2)
def reward(g, s_, done):
# return 1+1/episode_step if goal_reached(g, s_) else 0
# if done and not goal_reached(g, s_): return -1
return 1 if goal_reached(g, s_) else -1
#return -goal_distance(g, s_)
#if goal_reached(g, s_):
# return 1
# else:
# return -1
# for i in range(n_goals):
# if goal_reached(i, s_):
# if i == g: return 1
# else: return -1
# return 0
step = 0
score_list = []
avescore_list = []
testscore_list = []
flag = [False, False, False, False]
for episode in range(max_episode):
# initial observation
s = env.reset()
score = 0
g = meta_controller.choose_action(s)
Done = False
while True:
F = 0
s0 = s
episode_step = 1
while True:
env.render()
a = controller.choose_action(np.hstack((s, g)))
s_, f, done = env.step(a)
score += f
r = reward(g, s_, done)
controller.memory.add(np.hstack((s, g)), a, r, np.hstack((s_, g)), done)
if (step > controller_start) and (step % 5 == 0):
controller.learn()
# if (step > meta_controller_start) and (step % 5 == 0):
# meta_controller.learn()
if step == meta_controller_start:
print('\nmeta controller start learn~~~~~~~~~~~~~~~~~~~~')
if step == controller_start:
print('\ncontroller start learn~~~~~~~~~~~~~~~~~~~~~~~~~~')
F = F + f
s = s_
step = step + 1
if done:
Done = True
break
if goal_reached(g, s):
break
episode_step = episode_step + 1
if goal_reached(g, s):
meta_controller.memory.add(s0, g, F, s, done)
if step > meta_controller_start:
meta_controller.learn()
if Done:
break
g = meta_controller.choose_action(s)
# print(step)
score_list.append(score)
if (episode > 0 and episode % 50 == 0):
avescore = np.average(np.array(score_list[-50:]))
avescore_list.append(avescore)
print("\nepisode %d : average score = %f" % (episode, avescore))
testscore = play_maze()
testscore_list.append(testscore)
print("episode %d : test score = %f\n" % (episode, testscore))
if avescore > 2.5 and not flag[0]:
flag[0] = True
with open(file, 'a+') as f:
f.write('train average score achieves 2.5 (' + str(avescore) + ')\n')
# print('game over')
# env.destroy()
# return
elif avescore > 0.5 and not flag[1]:
flag[1] = True
with open(file, 'a+') as f:
f.write('train average score achieves 0.5 (' + str(avescore) + ')\n')
if testscore > 2.5 and not flag[2]:
flag[2] = True
with open(file, 'a+') as f:
f.write('test score achieves 2.5 (' + str(testscore) + ')\n')
elif testscore > 0.5 and not flag[3]:
flag[3] = True
with open(file, 'a+') as f:
f.write('test score achieves 0.5 (' + str(testscore) + ')\n')
if (episode > 0 and episode % 10 == 0):
if step > controller_start:
print('controller loss:', np.mean(controller.cost_his[np.max(
[0, controller.learn_step_counter - 100]):controller.learn_step_counter]))
if step > meta_controller_start:
print('meta controller loss:', np.mean(meta_controller.cost_his[np.max(
[0, meta_controller.learn_step_counter - 100]):meta_controller.learn_step_counter]))
# end of game
print('game over')
env.destroy()
plt.plot(range(len(avescore_list)), avescore_list)
plt.show()
np.savetxt("E:/my_py_project/rl/log/maze33/hdqnm/SEED_%d_avescore_log.txt" % (seed), avescore_list)
np.savetxt("E:/my_py_project/rl/log/maze33/hdqnm/SEED_%d_testscore_log.txt" % (seed), testscore_list)
env.after(100, run_maze, max_episode)
env.mainloop()
controller.plot_cost()
meta_controller.plot_cost()
import gym
from DQN_modified import DeepQNetwork
import matplotlib.pyplot as plt
import numpy as np
env = gym.make('CartPole-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=env.action_space.n,
n_features=env.observation_space.shape[0],
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)
# action_space=[[0.0, 0.8], [1.0, 0.0]]
# action_space = [[0.0, 1.0], [1.0, 0.8]]
# action_space = [[1.0, 1.0], [1.0, 0.0],[0.0,0.0],[0.0,1.0]]
# action_space=[[0.0,0.8],[0.0,1.0]]
# print(action_space)
# print(action_space[:4])
# print(action_space[5:])
# print(action_space[:-6])
# print(action_space[-5:])
envSeqDec = ChallengeProveEnvironment()
action_space=getActionSpace(envSeqDec)#缩减action space
print('actionSpace::::::::::::',action_space)
RL = DeepQNetwork(len(action_space), 6,
learning_rate=0.0001,
reward_decay=0.9,
e_greedy=0.9,
replace_target_iter=20,#200
memory_size=100,#2000
batch_size=16,
# output_graph=True
)
# print(RL)
# print('\n'.join(['%s:%s' % item for item in RL.__dict__.items()]))
rewards=run_maze()
print('Best Reward:',np.max(rewards))
x = list(range(len(rewards)))
plt.plot(x, rewards)
plt.show()
# env.mainloop()
# RL.plot_cost()
def train_dqn(seed, file):
np.random.seed(seed)
tf.set_random_seed(seed)
# dsdp game
env = dsdp()
max_episode = 20000
dqn_start = 5000
dqn = DeepQNetwork(env.n_actions,
env.n_features,
'dqn',
optimizer='rmsprop',
momentum=0.9,
learning_rate=0.00025,
opt_decay=0.99,
reward_decay=0.99,
e_greedy=0,
e_greedy_max=0.99,
e_greedy_increment=1e-4,
e_greedy_iter=5e3,
replace_target_iter=200,
memory_size=10000,
output_graph=False,
prioritized_replay=True,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=1e5,
prioritized_replay_eps=1e-6)
def play_maze():
# def goal_reached(g, s):
# if(g == 0):
# return (s[0]==0)
# else:
# return ((s[2*g-1]==0) and (s[2*g]==0))
def goal_reached(g, s):
return (s[1] == g)
s = env.reset()
score = 0
while True:
env.render()
a = dqn.choose_action(s, test=True)
s_, r, done = env.step(a)
score += r
if done:
break
s = s_
return score
def run_maze(max_episode):
step = 0
score_list = []
avescore_list = []
testscore_list = []
flag = [False, False, False, False]
for episode in range(max_episode):
# initial observation
s = env.reset()
score = 0
while True:
env.render()
a = dqn.choose_action(s)
s_, r, done = env.step(a)
score += r
dqn.memory.add(s, a, r, s_, done)
if (step > dqn_start) and (step % 5 == 0):
dqn.learn()
if step == dqn_start:
print('\ndqn start learn~~~~~~~~~~~~~~~~~~~~')
s = s_
step = step + 1
if done:
break
score_list.append(score)
if (episode > 0 and episode % 50 == 0):
# average score
avescore = np.average(np.array(score_list[-50:]))
avescore_list.append(avescore)
print("\nepisode %d : average score = %f" %
(episode, avescore))
# test score
testscore = 0
for i in range(5):
testscore += play_maze()
testscore /= 5
testscore_list.append(testscore)
print("episode %d : test score = %f\n" % (episode, testscore))
# logs
if avescore > 0.1 and not flag[0]:
flag[0] = True
with open(file, 'a+') as f:
f.write('train average score achieves 0.1 (' +
str(avescore) + ')\n')
#print('game over')
#env.destroy()
#return
elif avescore > 0.02 and not flag[1]:
flag[1] = True
with open(file, 'a+') as f:
f.write('train average score achieves 0.02 (' +
str(avescore) + ')\n')
if testscore > 0.1 and not flag[2]:
flag[2] = True
with open(file, 'a+') as f:
f.write('test score achieves 0.1 (' + str(testscore) +
')\n')
elif testscore > 0.02 and not flag[3]:
flag[3] = True
with open(file, 'a+') as f:
f.write('test score achieves 0.02 (' + str(testscore) +
')\n')
# loss
if (episode > 0 and episode % 10 == 0):
if step > dqn_start:
print(
'dqn loss:',
np.mean(
dqn.cost_his[np.
max([0, dqn.learn_step_counter -
100]):dqn.learn_step_counter]))
# end of game
print('game over')
env.destroy()
plt.plot(range(len(avescore_list)), avescore_list)
plt.show()
np.savetxt(
"E:/my_py_project/rl/log/dsdp/dqn/SEED_%d_avescore_log.txt" %
(seed), avescore_list)
np.savetxt(
"E:/my_py_project/rl/log/dsdp/dqn/SEED_%d_testscore_log.txt" %
(seed), testscore_list)
env.after(100, run_maze, max_episode)
env.mainloop()
dqn.plot_cost()
import gym
from DQN_modified 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)
step += 1
# end of game
print('game over')
# print(RL.memory)
env.destroy()
if __name__ == "__main__":
# maze game
env = Maze()
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
)
env.after(100, run_maze)
env.mainloop()
RL.plot_cost()
import matplotlib.pyplot as plt
plt.plot(np.arange(len(episode_reward)), episode_reward)
plt.ylabel('episode_reward')
plt.xlabel('run steps')
plt.show()
def translate_int_action(int_action):
act = np.zeros(2)
if int_action == 1:
act[0] = 1
if int_action == 2:
act[1] = 1
return act
env = Dogfight()
RL = DeepQNetwork(n_actions=3,
n_features=14,
learning_rate=0.01,
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)
from DQN_modified import DeepQNetwork
env = Crypto(name='BTC-USD',
data_path='./test.csv',
start_cash=1000,
fee=0.001,
drawdown_call=10,
fixed_stake=0.001,
period=180)
RL = DeepQNetwork(
env.n_actions,
env.n_features,
learning_rate=0.01,
reward_decay=0.9,
e_greedy=0.9,
e_greedy_increment=0.00001,
replace_target_iter=6400,
memory_size=100000,
# output_graph=True
)
total_steps = 0
total_length = env.length
for i_episode in range(total_length):
observation = env.reset()
ep_r = 0
while True:
# swap observation
observation = observation_
observation = observation_
# break while loop when end of this episode
if done:
break
step += 1
# end of game
print('game over')
env.destroy()
if __name__ == "__main__":
# maze game
env = Maze()
RL = DeepQNetwork(
env.n_actions,
env.n_features,
learning_rate=0.01,
reward_decay=0.9,
e_greedy=0.9,
replace_target_iter=200, #每200步替换一次target_net的参数
memory_size=2000, #记忆上线
# output_graph=True #是否输出tensorboard文件
)
env.after(100, run_maze)
env.mainloop()
RL.plot_cost()
