def main():
# Create carpole environment and network
env = gym.make('CartPole-v0').unwrapped
if not os.path.exists(model_path):
raise Exception("You should train the DQN first!")
net = DQN(n_state=env.observation_space.shape[0],
n_action=env.action_space.n,
epsilon=epsilon,
batch_size=batch_size,
model_path=model_path)
net.load()
net.cuda()
reward_list = []
for i in range(episode):
s = env.reset()
total_reward = 0
while True:
# env.render()
# Select action and obtain the reward
a = net.chooseAction(s)
s_, r, finish, _ = env.step(a)
total_reward += r
if finish:
print("Episode: %d \t Total reward: %d \t Eps: %f" %
(i, total_reward, net.epsilon))
reward_list.append(total_reward)
break
s = s_
env.close()
print("Testing average reward: ", np.mean(reward_list))
epsilon=epsilon,
epsilon_decay=epsilon_decay,
update_iter=update_iter,
batch_size=batch_size,
gamma=gamma,
model_path=model_path)
net.cuda()
net.load()
reward_list = []
for i in range(episode):
s = env.reset()
total_reward = 0
while True:
# env.render()
# Select action and obtain the reward
a = net.chooseAction(s)
s_, r, finish, info = env.step(a)
# Record the total reward
total_reward += r
# Revised the reward
if finish:
# 如果遊戲已結束，則將reward設為0以讓網路收斂
r = 0
else:
# ----------------------------------------------------
# 拆解reward，更精準的給予環境需要的訊息
# 1. r1得到的是對於距離的資訊，
# -abs term代表鼓勵agent不要去移動車子，
# 一直維持在中間才能獲得很高的獎賞！
