class GameManager():
def __init__(self):
# Init game state
self.episode = 0.0
self.win_counter = 0.0
self.state = CardGameState(self)
self.brain = DDQN()
self.episode_reward = 0
self.game_history = list()
def update(self, dt):
pass
def auto_play(self):
while self.episode < MAX_EPISODES:
action = self.brain.get_action(self.state)
action_to_store = np.zeros(3)
action_to_store[action] = 1
self.state.process(action)
# receive game result
reward = self.state.reward
done = self.state.terminal
self.episode_reward += reward
self.brain.train(self.state, self.state.s_t, action_to_store,
reward, self.state.s_t1, done)
self.state.t += 1
self.state.update()
if done:
self.episode += 1
win_rate = 0.0
if self.episode_reward == 1:
self.game_history.append(1)
else:
self.game_history.append(0)
if len(self.game_history) < GAME_HISTORY_SIZE:
win_rate = np.sum(self.game_history) / float(
len(self.game_history)) * 100.0
else:
self.game_history.pop(0)
win_rate = np.sum(
self.game_history) / GAME_HISTORY_SIZE * 100.0
print("Episode {} | Win Rate = {}".format(
self.episode, win_rate))
self.brain.write_summary(win_rate, self.episode)
self.episode_reward = 0
self.state.reset()
if done:
print('episode:',i_episode,'ep_r:',round(ep_r,2),'epsilon',round(RL.epsilon,2),'buffer_size:',RL.memory_count,'steps:',total_steps)
total_reward.append(ep_r)
break
s = s_
total_steps += 1
# Test every 100 episodes
if i_episode % 10 == 0:
total_rewards = 0
for i in range(TEST):
state = env.reset()
for j in range(STEP):
env.render()
action = RL.get_action(state) # direct action for test
state,reward,done,_ = env.step(action)
total_rewards += reward
if done:
break
ave_reward = total_rewards/TEST
print ('episode: ',i_episode,'Evaluation Average Reward:',ave_reward)
RL.plot_cost()
import matplotlib.pyplot as plt
import numpy as np
plt.plot(np.arange(len(total_reward)),total_reward)
plt.ylabel('Total Reward')
plt.xlabel('Episode ')
plt.show()