def main():
print('Cart Pole')
env = gym.make('CartPole-v1')
q_learn = QLearning(env, num_episodes=3000)
q_learn.run()
# Implement Q-learning and use this to solve the cartpole-environment
import gym
# Source: https://github.com/JoeSnow7/Reinforcement-Learning/blob/master/Cartpole%20Q-learning.ipynb
# We define a class to contain the learning algorithm
from QLearning import QLearning
env = gym.make("CartPole-v0")
agent = QLearning(env)
agent.train()
agent.run()
ui_ = UI(MATRIX, ui2sig_, sig2ui_) # UI是主进程
app_ = ui_.run_()
process_ = MoveUI(ui2sig_, sig2ui_) # MoveUI是子进程
process_.daemon = True
process_.start()
app_.exec_()
process_.terminate()
# %% Env + UI
if args_.env_ui:
env_ui(Env)
# %% QLearning + Env
if args_.q_env:
q_learning_ = QLearning(MATRIX) # QLearning是主进程
q_learning_.run(5)
# %% QLearning + UI
if args_.q_ui:
# q_learning_ = q_base_ui(QLearning, mute_train=args.mute_train)
q_learning_ = q_base_ui(
QLearning,
train_rounds=10,
e_schedule=(0.5, 0.95, 10),
test_rounds=30,
secs=0.03,
mute_train=args_.mute_train,
)
# %% Sarsa + Env
if args_.s_env:
