def update():
for episode in range(Max_Episodes):
# Initial oberservation
observation = env.reset()
while True:
# Fresh env
env.render()
# RL choose actions
action = RL.choose_action(str(observation))
# RL take action and get next observation and reward
observation_, reward, done = env.step(action)
# Save the transition from S to S'
RL.learn(str(observation), action, reward, str(observation_))
# Swap observation
observation = observation_
if done:
break
print('game over')
env.destroy()
if __name__ == "__main__":
env = Maze()
RL = QLearningTable(actions = list(range(env.n_actions)))
env.after(100, update)
env.mainloop()
RL.learn()
# swap 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,
memory_size=2000,
# output_graph=True
)
env.after(100, run_maze)
env.mainloop()
RL.plot_cost()
action = rl.choose_action(state_str, avaliable_actions) #选择动作
reward, state_next, is_success = maze.step(action) #获取环境奖励
state_next_str_arr = np.array(state_next) / maze.unit
state_next_str = str(state_next_str_arr.tolist())
rl.update(state_str, action, state_next_str, reward, is_success,
steps) #更新状态
steps.append([state_str, action]) #存储经历，后续学习
maze.render()
maze.write_weight(rl.state_table)
print('run end {0}'.format(j))
print(rl.state_table.round(2))
time.sleep(0.5)
# maze.write_weight(rl.state_table)
#进行强制更新，更新全部路径的权重多次训练之后就能够得到优秀权重
# rl.forceUpdate(steps,reward/10)
print("forceUpdate")
# print(rl.state_table.round(2))
if __name__ == '__main__':
maze = Maze(8, 6, chif=15)
rl = RLbrain(maze.get_all_action()) #获取所有动作
maze.after(100, render)
maze.mainloop()
for episode in range(100):
# initial observation
s_curr = maze._reset_maze()
while True:
maze._render()
# Get next action from the Q-table
action = rl.select_next_action(str(s_curr))
# take the action and observe the next state and reward
s_next, reward, isDone = maze._update_maze(action)
# learn from the feedback
rl.learn(str(s_curr), action, reward, str(s_next))
s_curr = s_next
if isDone:
break
print("Game over")
maze.destroy()
if __name__ == "__main__":
maze = Maze()
rl = QLearningTable(actions=list(range(len(maze.action_space))))
maze.after(100, update)
maze.mainloop()
s = env.reset()
while True:
env.render()
a = RL.choose_action(str(s))
s_, r, done = env.step(a)
RL.learn(str(s), a, r, str(s_))
# use a model to output (r, s_) by inputting (s, a)
# the model in dyna Q version is just like a memory replay buffer
env_model.store_transition(str(s), a, r, s_)
for n in range(10): # learn 10 more times using the env_model
ms, ma = env_model.sample_s_a() # ms in here is a str
mr, ms_ = env_model.get_r_s_(ms, ma)
RL.learn(ms, ma, mr, str(ms_))
s = s_
if done:
break
# end of game
print('game over')
env.destroy()
if __name__ == "__main__":
env = Maze()
RL = QLearningTable(actions=list(range(env.n_actions)))
env_model = EnvModel(actions=list(range(env.n_actions)))
env.after(0, update)
env.mainloop()
observation = env.reset()
while True:
# Fresh env
env.render()
# RL choose actions
action = RL.choose_action(str(observation))
# RL take action and get next observation and reward
observation_, reward, done = env.step(action)
# Save the transition from S to S'
RL.learn(str(observation), action, reward, str(observation_))
# Swap observation
observation = observation_
if done:
break
print('game over')
env.destroy()
if __name__ == "__main__":
env = Maze()
if Algorithm == 'SarsaLambda':
RL = SarsaLambda(action_space=list(range(env.n_actions)))
env.after(100, update_Sarsa)
elif Algorithm == 'Q_learning':
RL = Q_learning(action_space=list(range(env.n_actions)))
env.after(100, update)
elif Algorithm == 'Sarsa':
RL = Sarsa(action_space=list(range(env.n_actions)))
env.after(100, update_Sarsa)
env.mainloop()
