while (nb_games < nb_max):
if p.game_over(): #check if the game is over
if (len(agent.memory) > 1):
agent.replay(int(len(agent.memory) * 0.8))
scores.append(reward)
nb_games += 1
distance.append(step)
step = 0
p.reset_game()
if (nb_games % (10) == 0):
print(nb_games)
obs = process_obs(game.getGameState())
action = agent.act(obs)
reward = p.act(action) + step
step += 1
if p.game_over() is False:
agent.remember(obs, action, reward, process_obs(game.getGameState()),
p.game_over())
agent.save_alt()
plt.figure()
plt.plot(range(len(scores)), scores)
plt.show()
plt.figure()
plt.plot(range(len(distance)), distance)
plt.show()
agent = Human(actions=env.getActionSet())
env.init()
scores = []
min_reward = float('inf')
max_reward = float('-inf')
while True:
episode_reward = 0.0
print 'Press s to start'
while not keyboard.is_pressed('s'):
continue
while not env.game_over():
state = game.getGameState()
action = agent.pickAction(state)
reward = env.act(action)
episode_reward += reward
print 'Player score: {:0.1f}.'.format(episode_reward)
scores.append(episode_reward)
min_reward = min(min_reward, episode_reward)
max_reward = max(max_reward, episode_reward)
answer = raw_input('Keep playing ("y" to continue)? ')
if answer is not 'y':
break
env.reset_game()
print 'Average score for %d runs: {:0.1f}'.format(
np.mean(scores)) % len(scores)
scores = []
agent = DQL.deep_learner(1, 2, p.getActionSet())
agent.learning_rate = 0.001
agent.epsilon = 0.001
agent.epsilon_decay = 1.0
agent.epsilon_min = 0.00001
#agent.load_model_json()
agent.load_alt()
nb_games = 0
for layer in agent.model.layers:
print(layer.get_config())
while (nb_games < 20):
if p.game_over(): #check if the game is over
scores.append(game.getScore())
nb_games += 1
p.reset_game()
if (nb_games % (50) == 0):
print(nb_games)
obs = process_obs(game.getGameState())
action = agent.act(obs)
reward = p.act(action)
plt.plot(range(len(scores)), scores)
plt.show()