class PLEEnv(Env):
def __init__(self, game, _id, render=True, reset_done=True, num_steps=100):
super().__init__(_id, render, reset_done)
self.num_steps = num_steps
self.game = game
self.start()
def start(self):
if not self.env_instance:
self.env_instance = PLE(self.game,
fps=30,
display_screen=self.render)
self.env_instance.init()
def step(self, action):
reward = self.env_instance.act(action)
obs = self.env_instance.getGameState()
done = self.env_instance.game_over()
return obs, reward, done
def reset(self):
self.env_instance.reset_game()
obs = self.env_instance.getGameState()
return obs
def close(self):
pass
def restart(self):
self.close()
self.reset()
def evaluate(agent):
env = PLE(game, fps=30, display_screen=True)
actionset = env.getActionSet()
eval_reward = []
for i in range(5):
env.init()
env.reset_game()
obs = list(env.getGameState().values())
episode_reward = 0
while True:
action = agent.predict(obs)
observation = env.getScreenRGB()
score = env.score()
#action = agent.pickAction(reward, observation)
observation = cv2.transpose(observation)
font = cv2.FONT_HERSHEY_SIMPLEX
observation = cv2.putText(observation, str(int(score)), (0, 25),
font, 1.2, (255, 255, 255), 2)
cv2.imshow("ss", observation)
cv2.waitKey(10) # 预测动作，只选最优动作
reward = env.act(actionset[action])
obs = list(env.getGameState().values())
done = env.game_over()
episode_reward += reward
if done:
break
eval_reward.append(episode_reward)
cv2.destroyAllWindows()
return np.mean(eval_reward)
class PleEnvAdapter(EnvAdapter):
"""Pygame learning env adapter"""
def __init__(self, *args, **kwargs):
super(PleEnvAdapter, self).__init__(*args, **kwargs)
if not self.render:
os.putenv('SDL_VIDEODRIVER', 'fbcon')
os.environ["SDL_VIDEODRIVER"] = "dummy"
Game = envs_lookup_table[self.env_name]
self.env = PLE(Game(),
display_screen=self.render,
force_fps=not self.render)
self.env.init()
def get_input_shape(self):
return (len(self.env.getGameState()), )
def reset(self):
self.env.reset_game()
def step(self, action) -> (object, float, bool):
reward = self.env.act(self.env.getActionSet()[action])
observation = self.env.getGameState()
observation = [val for key, val in observation.items()]
done = self.env.game_over()
return observation, reward, done
def get_n_actions(self) -> int:
return len(self.env.getActionSet())
def get_random_action(self):
return random.randint(0, len(self.env.getActionSet()) - 1)
def test_movement_up():
game = Pong()
p = PLE(game, display_screen=True, fps=20, force_fps=1)
p.init()
time.sleep(.5)
oldState = p.getGameState()
p.act(game.actions["up"])
newState = p.getGameState()
assert oldState["player_velocity"] > newState["player_velocity"]
class Game(gym.Env):
def __init__(self, display_screen=False, force_fps=True):
os.environ["SDL_VIDEODRIVER"] = "dummy"
game = FlappyBird() # define and initiate the environment
self.env = PLE(game,
fps=30,
display_screen=display_screen,
force_fps=force_fps)
self.env.init()
# list of actions in the environment
self.actions = self.env.getActionSet()
# length of actions
self.action_space = spaces.Discrete(len(self.actions))
def step(self, action):
"""Take the action chosen and update the reward"""
reward = self.env.act(self.actions[action])
state = self.getGameState()
terminal = self.env.game_over()
# If the bird is stuck, the game is over and a reward of -1000
# if it continues, +1
if terminal:
reward = -1000
else:
reward = 1
return state, reward, terminal, {}
def getGameState(self):
'''
PLEenv return gamestate as a dictionary. Returns a modified form
of the gamestate only with the required information to define the state
'''
state = self.env.getGameState()
h_dist = state['next_pipe_dist_to_player']
v_dist = state['next_pipe_bottom_y'] - state['player_y']
vel = state['player_vel']
return ' '.join([str(vel), str(h_dist), str(v_dist)])
def reset(self):
"""Resets the game to start a new game"""
self.env.reset_game()
state = self.env.getGameState()
return state
def seed(self, seed):
rng = np.random.RandomState(seed)
self.env.rng = rng
self.env.game.rng = self.env.rng
self.env.init()
def test():
# 创建环境
game = FlappyBird()
env = PLE(game, fps=30, display_screen=True)
obs_dim = len(env.getGameState())
act_dim = len(env.getActionSet())
print('action set:', env.getActionSet())
logger.info('obs_dim {}, act_dim {}'.format(obs_dim, act_dim))
# 创建经验池
rpm = ReplayMemory(MEMORY_SIZE) # DQN的经验回放池
# 根据parl框架构建agent
model = Model(act_dim=act_dim)
algorithm = DQN(model, act_dim=act_dim, gamma=GAMMA, lr=LEARNING_RATE)
agent = Agent(algorithm,
obs_dim=obs_dim,
act_dim=act_dim,
e_greed=0.3,
e_greed_decrement=1e-6)
# 加载模型
save_path = './DQN/checkpoints/episode_V14600.ckpt'
print('checkpoints:', save_path)
if os.path.exists(save_path):
logger.info('load ckpt success!')
agent.restore(save_path)
else:
logger.error('load ckpt error!')
action_set = env.getActionSet()
env.init()
episode_reward = 0
steps = 0
while not env.game_over():
steps += 1
if (steps == 1):
continue
obs = list(env.getGameState().values())
action_idx = agent.predict(obs) # 预测动作，只选最优动作
act = action_set[action_idx]
reward = env.act(act)
episode_reward += reward
reward_str = str(int(episode_reward))
drawText(env.game.screen, reward_str, 288, 0, 48, (255, 0, 0),
(255, 255, 255))
env.reset_game()
logger.info('[Test] steps:{}, reward:{}'.format(steps, episode_reward))
def main():
env = FlappyBird()
penv = PLE(env, fps=30, display_screen=True, force_fps=True)
#penv.init()
np.random.seed(0)
obs_shape = len(penv.getGameState())
IMG_shape = penv.getScreenGrayscale().shape
action_dim = len(penv.getActionSet())
print(obs_shape, action_dim)
rpm = ReplayMemory(MEMORY_SIZE)
model = Model(act_dim=action_dim)
algorithm = parl.algorithms.DQN(model,
act_dim=action_dim,
gamma=GAMMA,
lr=LEARNING_RATE)
agent = Agent(
algorithm,
obs_dim=obs_shape,
act_dim=action_dim,
e_greed=0.15, # explore 0.1
e_greed_decrement=1e-6 #1e-6
) # probability of exploring is decreasing during training
# 加载模型
if os.path.exists('./dqn_model.ckpt'):
save_path = './dqn_model.ckpt'
agent.restore(save_path)
print("模型加载成功")
eval_reward = evaluate(agent, penv)
class Env:
def __init__(self):
# initializing the instance of FlappyBird class
self.game = FlappyBird(pipe_gap=100)
# then pass this object into PLE constructor and create an instance of that
self.env = PLE(self.game, fps=30, display_screen=False)
# init does some necessary things under the hood
self.env.init()
self.env.getGameState = self.game.getGameState # maybe not necessary
self.action_map = self.env.getActionSet()
# function which takes an action
def step(self, action):
action = self.action_map[action]
reward = self.env.act(action)
done = self.env.game_over()
obs = self.get_observation()
return obs, reward, done
def reset(self):
self.env.reset_game()
return self.get_observation()
def get_observation(self):
# game state returns a dictionary which describes
# the meaning of each value
# we only want the values
obs = self.env.getGameState()
return np.array(list(obs.values()))
def set_display(self, boolean_value):
self.env.display_screen = boolean_value
class Env:
def __init__(self):
self.game = FlappyBird(pipe_gap=125)
self.env = PLE(self.game, fps=30, display_screen=True)
self.env.init()
self.env.getGameState = self.game.getGameState # maybe not necessary
# by convention we want to use (0,1)
# but the game uses (None, 119)
self.action_map = self.env.getActionSet() #[None, 119]
def step(self, action):
action = self.action_map[action]
reward = self.env.act(action)
done = self.env.game_over()
obs = self.get_observation()
# don't bother returning an info dictionary like gym
return obs, reward, done
def reset(self):
self.env.reset_game()
return self.get_observation()
def get_observation(self):
# game state returns a dictionary which describes
# the meaning of each value
# we only want the values
obs = self.env.getGameState()
return np.array(list(obs.values()))
def set_display(self, boolean_value):
self.env.display_screen = boolean_value
def main_test():
final_score = 0
previous_action = 1
model = build_neural_network_model()
game = FlappyBird(width=288, height=512, pipe_gap=100)
env = PLE(game, fps=30, display_screen=True, state_preprocessor=process_state)
model = load_model("model.h5")
env.init()
passed = 0
old_y = 0
for i in range(game_steps):
if i == game_steps - 1:
print("Score: {}".format(final_score))
if env.game_over():
print("Final Score: {}".format(final_score))
time.sleep(1)
final_score = 0
env.reset_game()
observation = env.getGameState()
vector = model.predict(np.matrix(list(observation[0].values())))
a_star = np.argmax(vector[0])
print(vector[0][0], vector[0][1], a_star)
time.sleep(0.05)
env_reward = env.act(env.getActionSet()[a_star])
if env_reward == 1:
final_score += 1
class Env:
def __init__(self):
self.game = FlappyBird(pipe_gap=110)
self.env = PLE(self.game, fps=30, display_screen=False)
self.env.init()
self.env.getGameState = self.game.getGameState # maybe not necessary
# by convention we want to use (0,1)
# but the game uses (None, 119)
self.action_map = self.env.getActionSet() # [None, 119]
def step(self, action):
action = self.action_map[action]
reward = self.env.act(action)
done = self.env.game_over()
obs = self.get_observation()
return obs, reward, done
def reset(self):
self.env.reset_game()
return self.get_observation()
def get_observation(self):
# game state returns a dictionary which describes
# the meaning of each value
# we only want the values
obs = self.env.getGameState()
return np.array(list(obs.values()))
def set_display(self, boolean_value):
self.env.display_screen = boolean_value
def main():
env = FlappyBird()
penv = PLE(env, fps=30, display_screen=True,force_fps=True)
#penv.init()
np.random.seed(0)
obs_shape = len(penv.getGameState())
IMG_shape = penv.getScreenGrayscale().shape
action_dim = len(penv.getActionSet())
print(obs_shape,action_dim)
rpm = ReplayMemory(MEMORY_SIZE)
model = Model(act_dim=action_dim)
algorithm = parl.algorithms.DQN(
model, act_dim=action_dim, gamma=GAMMA, lr=LEARNING_RATE)
agent = Agent(
algorithm,
obs_dim=obs_shape,
act_dim=action_dim,
e_greed=0.15, # explore 0.1
e_greed_decrement=1e-6 #1e-6
) # probability of exploring is decreasing during training
# 加载模型
if os.path.exists('./dqn_model.ckpt'):
save_path = './dqn_model.ckpt'
agent.restore(save_path)
print("模型加载成功")
while len(rpm) < MEMORY_WARMUP_SIZE: # warm up replay memory
run_episode(agent, penv, rpm)
max_episode = 1000
# start train
episode = 0
while episode < max_episode:
# train part
for i in range(0, 50):
total_reward = run_episode(agent, penv, rpm)
episode += 1
eval_reward = evaluate(agent, penv)
logger.info('episode:{} test_reward:{}'.format(
episode, eval_reward))
# 训练结束，保存模型
save_path = './model/dqn_model_{}_{}.ckpt'.format(episode, eval_reward)
agent.save(save_path)
# 训练结束，保存模型
save_path = './dqn_model.ckpt'
agent.save(save_path)
def test():
game = FlappyBird()
env = PLE(game, fps=30, display_screen=True)
obs_dim = len(env.getGameState())
action_dim = 2 # 只能是up键，还有一个其它，所以是2
model = Model(act_dim=action_dim)
algorithm = parl.algorithms.DQN(model,
act_dim=action_dim,
gamma=GAMMA,
lr=LEARNING_RATE)
agent = Agent(
algorithm,
obs_dim=obs_dim,
act_dim=action_dim,
e_greed=0.2, # explore
e_greed_decrement=1e-6)
if os.path.exists('./model_dir'):
agent.restore('./model_dir')
# test part, run 5 episodes and average
eval_reward = []
for i in range(5):
env.init()
episode_reward = 0
isOver = False
step = 0
while not isOver:
if step == 0:
reward = env.act(None)
done = False
else:
time.sleep(0.01) #windows running too fast, slow it down
obs = list(env.getGameState().values())
action = agent.predict(obs)
if action == 1:
act = actions["up"]
else:
act = None
reward = env.act(act)
isOver = env.game_over()
episode_reward += reward
step += 1
eval_reward.append(episode_reward)
if step > MAX_STEP:
break
env.reset_game()
return np.mean(eval_reward)
def train():
# 创建环境
game = FlappyBird()
env_1 = PLE(game, fps=30, display_screen=False)
env_2 = PLE(game, fps=30, display_screen=True)
obs_dim = len(env_1.getGameState())
act_dim = len(env_1.getActionSet())
print('action set:', env_1.getActionSet())
logger.info('obs_dim {}, act_dim {}'.format(obs_dim, act_dim))
# 创建经验池
rpm = ReplayMemory(MEMORY_SIZE) # DQN的经验回放池
# 根据parl框架构建agent
model = Model(act_dim=act_dim)
algorithm = DQN(model, act_dim=act_dim, gamma=GAMMA, lr=LEARNING_RATE)
agent = Agent(algorithm,
obs_dim=obs_dim,
act_dim=act_dim,
e_greed=0.3,
e_greed_decrement=1e-6)
# 加载模型
save_path = './flappybird.ckpt'
if os.path.exists(save_path):
agent.restore(save_path)
# 先往经验池里存一些数据，避免最开始训练的时候样本丰富度不够
while len(rpm) < MEMORY_WARMUP_SIZE:
run_episode(env_1, agent, rpm)
max_episode = 2000
# 开始训练
episode = 0
while episode < max_episode: # 训练max_episode个回合，test部分不计算入episode数量
# train
for i in range(0, 100):
total_reward, steps = run_episode(env_1, agent, rpm)
episode += 1
# test
eval_reward, steps = evaluate(env_2, agent)
logger.info(
'[episode:{}], e_greed:{:.6f}, steps:{}, test_reward:{}'.format(
episode, agent.e_greed, steps, eval_reward))
# 保存模型
ckpt = './models/episode_{}.ckpt'.format(episode)
agent.save(ckpt)
# 训练结束，保存模型
save_path = './flappybird.ckpt'
agent.save(save_path)
def test():
game = Snake(600, 600)
p = PLE(game,
fps=60,
state_preprocessor=process_state,
force_fps=True,
display_screen=True,
frame_skip=2,
reward_values={
"positive": 100.0,
"negative": -50.0,
"tick": -0.1,
"loss": -70.0,
"win": 5.0
})
agent = Agent(alpha=float(sys.argv[1]),
gamma=float(sys.argv[2]),
n_actions=3,
epsilon=0.01,
batch_size=100,
input_shape=6,
epsilon_dec=0.99999,
epsilon_end=0.001,
memory_size=500000,
file_name=sys.argv[3],
activations=[str(sys.argv[4]),
str(sys.argv[5])])
p.init()
agent.load_game()
scores = []
for _ in range(200):
if p.game_over():
p.reset_game()
apples = 0
initial_direction = "Right"
while not p.game_over():
old_state = np.array(
vision(list(p.getGameState()[0]), initial_direction))
action = agent.choose_action(old_state)
possible_directions = prepare_corect_directions(initial_direction)
possible_directions_tuples = list(
zip(possible_directions.keys(), possible_directions.values()))
direction = possible_directions_tuples[action]
initial_direction = direction[1]
reward = p.act(direction[0])
if reward > 50.0:
apples += reward
scores.append(apples)
return scores
def main(train=False):
# Don't modify anything in this function.
# See the constants defined at the top of this file if you'd like to
# change the FPS, screen size, or round length
game = Pong(width=WIDTH, height=HEIGHT, MAX_SCORE=MAX_SCORE)
if train:
p = PLE(game, fps=FPS, display_screen=False, force_fps=True)
else:
p = PLE(game, fps=FPS, display_screen=True, force_fps=False)
p.init()
agent_rounds = 0
cpu_rounds = 0
agent_score = 0
cpu_score = 0
num_frames = 0
while True:
if p.game_over():
if game.score_counts['agent'] > game.score_counts['cpu']:
agent_rounds += 1
print('AGENT won round')
else:
cpu_rounds += 1
print('CPU won round')
if agent_rounds == NUM_ROUNDS or cpu_rounds == NUM_ROUNDS:
break
p.reset_game()
obs = p.getGameState()
action = agent(normalize(obs))
reward = p.act(ACTION_MAP[action])
if reward > 0:
agent_score += 1
print('AGENT scored')
elif reward < 0:
cpu_score += 1
print('CPU scored')
num_frames += 1
winner = 'AGENT' if agent_rounds > cpu_rounds else 'CPU'
print('Winner:', winner)
print('Num frames :', num_frames)
print('AGENT rounds won:',agent_rounds)
print('CPU rounds won:',cpu_rounds)
print('AGENT total score:',agent_score)
print('CPU total score:',cpu_score)
def main():
env = PLE(Pixelcopter(),
fps=30,
display_screen=True,
state_preprocessor=None)
action_dim = len(env.getActionSet())
obs_shape = len(env.getGameState())
rpm = ReplayMemory(MEMORY_SIZE) # DQN的经验回放池
# 根据parl框架构建agent
model = Model(act_dim=action_dim)
algorithm = DQN(model, act_dim=action_dim, gamma=GAMMA, lr=LEARNING_RATE)
agent = Agent(
algorithm,
obs_dim=obs_shape,
act_dim=action_dim,
e_greed=0.1, # 有一定概率随机选取动作，探索
e_greed_decrement=1e-6) # 随着训练逐步收敛，探索的程度慢慢降低
# 加载模型
# save_path = './dqn_model.ckpt'
# agent.restore(save_path)
# 先往经验池里存一些数据，避免最开始训练的时候样本丰富度不够
while len(rpm) < MEMORY_WARMUP_SIZE:
run_episode(env, agent, rpm)
max_episode = 30000
# 开始训练
episode = 0
while episode < max_episode: # 训练max_episode个回合，test部分不计算入episode数量
# train part
for i in range(0, 50):
total_reward = run_episode(env, agent, rpm)
episode += 1
# test part
eval_reward, max_reward = evaluate(env, agent,
render=False) # render=True 查看显示效果
logger.info(
'episode:{} e_greed:{} test_reward:{} max_reward:{}'.format(
episode, agent.e_greed, eval_reward, max_reward))
# 训练结束，保存模型
save_path = './dqn_model.ckpt'
agent.save(save_path)
def view_agent(agent):
game = FlappyBird()
p = PLE(game, fps=30, display_screen=True)
p.init()
for i in range(200000):
if p.game_over():
p.reset_game()
time.sleep(0.03)
action = agent.pick_action(p.getGameState())
p.act(action)
if p.game_over():
break
def main():
game = FlappyBird()
env = PLE(game, fps=30, display_screen=False)
env.init()
action_dim = len(env.getActionSet())
obs_shape = len(env.getGameState())
rpm = ReplayMemory(MEMORY_SIZE)
model = FlappyBirdModel(act_dim=action_dim)
algorithm = parl.algorithms.DQN(model, act_dim=action_dim, gamma=GAMMA, lr=LEARNING_RATE)
agent = FlappyBirdAgent(
algorithm,
obs_dim=obs_shape,
act_dim=action_dim,
e_greed=0.2,
e_greed_decrement=1e-6
) # probability of exploring is decreasing during training
while len(rpm) < MEMORY_WARMUP_SIZE: # warm up replay memory
run_episode(agent, env, rpm)
max_episode = 50000
# start train
episode = 0
while episode < max_episode:
# train part
for i in range(0, 100):
total_reward = run_episode(agent, env, rpm)
episode += 1
# evaluation part
eval_reward = evaluate(agent, env)
logger.info('episode:{} test_reward:{}'.format(episode, eval_reward))
# learning rate adjustment
if episode % 100 == 0:
if algorithm.lr >= 5e-4:
algorithm.lr *= 0.995
if algorithm.lr <= 5e-4 and algorithm.lr >= 1e-4:
algorithm.lr *= 0.99
print('learning rate:', algorithm.lr)
# save model
save_path = './fb_dqn_model.ckpt'
agent.save(save_path)
def __init__(self, game, size=10, mutationrate=0.1, crossoverrate=0.1):
p = PLE(game, fps=30, display_screen=False)
p.init()
self.population = np.array([
NNAgent(inputs=p.getGameState().keys(), actions=p.getActionSet())
for i in range(size)
])
for agent in self.population:
agent.fitness = None
self.fitness = [] * size
self.generation = 0
self.popsize = size
self.mutationrate = mutationrate
self.crossoverrate = crossoverrate
def main():
game = FlappyBird()
env = PLE(game, fps=30, display_screen=False)
env_evaluate = PLE(game, fps=30, display_screen=False)
obs_dim = len(env.getGameState())
action_dim = 2 # 只能是up键，还有一个其它，所以是2
# rpm = ReplayMemory(MEMORY_SIZE, obs_dim, action_dim)
rpm = ReplayMemory(MEMORY_SIZE)
model = Model(act_dim=action_dim)
algorithm = parl.algorithms.DQN(model,
act_dim=action_dim,
gamma=GAMMA,
lr=LEARNING_RATE)
agent = Agent(
algorithm,
obs_dim=obs_dim,
act_dim=action_dim,
e_greed=0.2, # explore
e_greed_decrement=1e-6
) # probability of exploring is decreasing during training
if os.path.exists('./model_dir'):
agent.restore('./model_dir')
# while rpm.size() < MEMORY_WARMUP_SIZE: # warm up replay memory
while len(rpm) < MEMORY_WARMUP_SIZE: # warm up replay memory
run_episode(agent, env, rpm)
max_episode = 5000
# start train
episode = 0
while episode < max_episode:
# train part
for i in range(0, 50):
total_reward = run_episode(agent, env, rpm)
episode += 1
eval_reward = evaluate(agent, env_evaluate)
logger.info('episode:{} test_reward:{}'.format(
episode, eval_reward))
agent.save('./model_dir')
def play(file_name, number_of_games=1):
game = FlappyBird(width=game_width,
height=game_height,
pipe_gap=game_pipe_gap)
p = PLE(game, display_screen=True, force_fps=False, frame_skip=6)
p.init()
network = Network()
network.load(file_name, rename=False)
for i in range(number_of_games):
if i > 0:
p.reset_game()
while not p.game_over():
state = p.getGameState()
actions_q_values = network.Q(state).tolist()
action_taken_index = np.argmax(actions_q_values)
p.act(None if action_taken_index == 0 else 119)
def play_with_saved_agent(agent_file_path, agent_file_name, test_rounds=20):
game = RunningMinion()
env = PLE(game, fps=30, display_screen=True, force_fps=True, state_preprocessor=process_state)
my_agent = load_agent(env, agent_file_path, agent_file_name)
env.init()
print "Testing model:", agent_file_name
total_reward = 0.0
for _ in range(test_rounds):
my_agent.start_episode()
episode_reward = 0.0
while env.game_over() == False:
state = env.getGameState()
reward, action = my_agent.act(state, epsilon=0.00)
episode_reward += reward
print "Agent score {:0.1f} reward for episode.".format(episode_reward)
total_reward += episode_reward
my_agent.end_episode()
return total_reward/test_rounds
def main(w, seed=SEED, headless=False):
"""
Let an agent play flappy bird
"""
if headless:
display_screen = False
force_fps = True
else:
display_screen = True
force_fps = False
game = PLE(FLAPPYBIRD,
display_screen=display_screen,
force_fps=force_fps,
rng=seed)
game.init()
game.reset_game()
FLAPPYBIRD.rng.seed(seed)
agent_score = 0
num_frames = 0
while True:
if game.game_over():
break
obs = game.getGameState()
x = normalize(obs)
action = agent(x, w)
reward = game.act(ACTION_MAP[action])
if reward > 0:
agent_score += 1
num_frames += 1
print('Frames :', num_frames)
print('Score :', agent_score)
def play_with_saved_agent(agent_file_path, agent_file_name, test_rounds=20):
game = FlappyBird()
env = PLE(game, fps=30, display_screen=True, force_fps=True, state_preprocessor=process_state)
my_agent = load_agent(env, agent_file_path, agent_file_name)
env.init()
print "Testing model:", agent_file_name
total_reward = 0.0
for _ in range(test_rounds):
my_agent.start_episode()
episode_reward = 0.0
while env.game_over() == False:
state = env.getGameState()
reward, action = my_agent.act(state, epsilon=0.05)
episode_reward += reward
print "Agent score {:0.1f} reward for episode.".format(episode_reward)
total_reward += episode_reward
my_agent.end_episode()
return total_reward/test_rounds
#coding:utf-8
from ple.games.pong import Pong
from ple import PLE
import numpy as np
def get_obs(env):
# game_state = env.getGameState()
# obs = list(game_state.values())
obs = env.getScreenGrayscale()/255.0
return obs.astype(np.float).ravel()
if __name__ == '__main__':
game = Pong(width=128, height=96,MAX_SCORE=11)
p = PLE(game, fps=30, display_screen=True, force_fps=True)
# 根据parl框架构建agent
print(p.getActionSet())
act_dim = len(p.getActionSet())
p.getScreenGrayscale()
game_state = p.getGameState()
print(game_state)
if __name__ == "__main__":
game = Catcher(width=320, height=320)
env = PLE(game, display_screen=True, state_preprocessor=process_state)
agent = DQNAgent(env)
agent.load("./save/catcher.h5")
#초기화
#pylab.title("reward")
#pylab.xlabel("episodes")
#pylab.ylabel("rewards")
env.init()
scores, time = [], []
for e in range(EPISODES):
env.reset_game()
state = env.getGameState()
state = np.array([list(state[0])])
score = 0
for time_t in range(20000):
action = agent.act(state)
reward = env.act(action) #액션 선택
score += reward
next_state = env.getGameState()
next_state = np.array([list(next_state[0])])
action = [K_a, None, K_d].index(action)
agent.remember(state, action, reward, next_state, env.game_over())
state = next_state
def agent_training(agent_file_path, agent_file_name, fig_path, num_steps_train_total = 5000):
# training parameters
num_epochs = 5
num_steps_train_epoch = num_steps_train_total/num_epochs # steps per epoch of training
num_steps_test = 100
update_frequency = 10 # step frequency of model training/updates
epsilon = 0.15 # percentage of time we perform a random action, help exploration.
epsilon_steps = 1000 # decay steps
epsilon_min = 0.1
epsilon_rate = (epsilon - epsilon_min) / epsilon_steps
# memory settings
max_memory_size = 10000
min_memory_size = 60 # number needed before model training starts
game = RunningMinion()
env = PLE(game, fps=30, display_screen=True, force_fps=True, state_preprocessor=process_state)
my_agent = init_agent(env)
memory = utils.ReplayMemory(max_memory_size, min_memory_size)
env.init()
# Logging configuration and figure plotting
logging.basicConfig(filename='../learning.log', filemode='w',
level=logging.DEBUG, format='%(levelname)s:%(message)s')
logging.info('========================================================')
logging.info('Training started for total training steps: '+str(num_steps_train_total)+'.\n')
learning_rewards = [0]
testing_rewards = [0]
for epoch in range(1, num_epochs + 1):
steps, num_episodes = 0, 0
losses, rewards = [], []
env.display_screen = False
# training loop
while steps < num_steps_train_epoch:
episode_reward = 0.0
my_agent.start_episode()
while env.game_over() == False and steps < num_steps_train_epoch:
state = env.getGameState()
reward, action = my_agent.act(state, epsilon=epsilon)
memory.add([state, action, reward, env.game_over()])
if steps % update_frequency == 0:
loss = memory.train_agent_batch(my_agent)
if loss is not None:
losses.append(loss)
epsilon = np.max(epsilon_min, epsilon - epsilon_rate)
episode_reward += reward
steps += 1
if steps < num_steps_train_epoch:
learning_rewards.append(episode_reward)
if num_episodes % 5 == 0:
# print "Episode {:01d}: Reward {:0.1f}".format(num_episodes, episode_reward)
logging.info("Episode {:01d}: Reward {:0.1f}".format(num_episodes, episode_reward))
rewards.append(episode_reward)
num_episodes += 1
my_agent.end_episode()
logging.info("Train Epoch {:02d}: Epsilon {:0.4f} | Avg. Loss {:0.3f} | Avg. Reward {:0.3f}\n"
.format(epoch, epsilon, np.mean(losses), np.sum(rewards) / num_episodes))
steps, num_episodes = 0, 0
losses, rewards = [], []
# testing loop
while steps < num_steps_test:
episode_reward = 0.0
my_agent.start_episode()
while env.game_over() == False and steps < num_steps_test:
state = env.getGameState()
reward, action = my_agent.act(state, epsilon=0.05)
episode_reward += reward
testing_rewards.append(testing_rewards[-1]+reward)
steps += 1
# done watching after 500 steps.
if steps > 500:
env.display_screen = False
if num_episodes % 5 == 0:
logging.info("Episode {:01d}: Reward {:0.1f}".format(num_episodes, episode_reward))
if steps < num_steps_test:
testing_rewards.append(episode_reward)
rewards.append(episode_reward)
num_episodes += 1
my_agent.end_episode()
logging.info("Test Epoch {:02d}: Best Reward {:0.3f} | Avg. Reward {:0.3f}\n"
.format(epoch, np.max(rewards), np.sum(rewards) / num_episodes))
logging.info("Training complete.\n\n")
plot_figure(fig_path, learning_rewards, 'reward', 'reward_in_training', num_steps_train_total)
plot_figure(fig_path, testing_rewards, 'reward', 'reward_in_testing', num_steps_train_total)
save_agent(my_agent, agent_file_path, agent_file_name)
memory = ReplayMemory(max_memory_size, min_memory_size)
env.init()
for epoch in range(1, num_epochs+1):
steps, num_episodes = 0, 0
losses, rewards = [], []
env.display_screen = False
#training loop
while steps < num_steps_train:
episode_reward = 0.0
agent.start_episode()
while env.game_over() == False and steps < num_steps_train:
state = env.getGameState()
reward, action = agent.act(state, epsilon=epsilon)
memory.add([ state, action, reward, env.game_over() ])
if steps % update_frequency == 0:
loss = memory.train_agent_batch(agent)
if loss is not None:
losses.append(loss)
epsilon = np.max(epsilon_min, epsilon - epsilon_rate)
episode_reward += reward
steps += 1
if num_episodes % 5 == 0:
memory = ReplayMemory(max_memory_size, min_memory_size)
env.init()
for epoch in range(1, num_epochs + 1):
steps, num_episodes = 0, 0
losses, rewards = [], []
env.display_screen = False
# training loop
while steps < num_steps_train:
episode_reward = 0.0
agent.start_episode()
while env.game_over() == False and steps < num_steps_train:
state = env.getGameState()
reward, action = agent.act(state, epsilon=epsilon)
memory.add([state, action, reward, env.game_over()])
if steps % update_frequency == 0:
loss = memory.train_agent_batch(agent)
if loss is not None:
losses.append(loss)
epsilon = np.max(epsilon_min, epsilon - epsilon_rate)
episode_reward += reward
steps += 1
if num_episodes % 5 == 0:
print "Episode {:01d}: Reward {:0.1f}".format(
import numpy as np
from flappy_bird_model import FlappyBirdModel
from flappy_bird_agent import FlappyBirdAgent
from ple.games.flappybird import FlappyBird
from ple import PLE
LEARNING_RATE = 0.001
GAMMA = 0.99
game = FlappyBird()
env = PLE(game, fps=30, display_screen=False)
action_dim = len(env.getActionSet())
obs_shape = len(env.getGameState())
model = FlappyBirdModel(act_dim=action_dim)
algorithm = parl.algorithms.DQN(model,
act_dim=action_dim,
gamma=GAMMA,
lr=LEARNING_RATE)
agent = FlappyBirdAgent(algorithm,
obs_dim=obs_shape,
act_dim=action_dim,
e_greed=0.2,
e_greed_decrement=1e-6)
# load model
save_path = './fb_dqn_model.ckpt'
agent.restore(save_path)
class Agent:
LEARNING_RATE = 1e-6
BATCH_SIZE = 32
OUTPUT_SIZE = 2
EPSILON = 1
DECAY_RATE = 0.005
MIN_EPSILON = 0.1
GAMMA = 0.99
MEMORIES = deque()
MEMORY_SIZE = 300
COPY = 1000
T_COPY = 0
INITIAL_IMAGES = np.zeros((80, 80, 4))
# based on documentation, features got 8 dimensions
# output is 2 dimensions, 0 = do nothing, 1 = jump
def __init__(self, screen=False, forcefps=True):
self.game = FlappyBird(pipe_gap=125)
self.env = PLE(self.game,
fps=30,
display_screen=screen,
force_fps=forcefps)
self.env.init()
self.env.getGameState = self.game.getGameState
self.model = Model(self.OUTPUT_SIZE, self.LEARNING_RATE)
self.model_negative = Model(self.OUTPUT_SIZE, self.LEARNING_RATE)
self.sess = tf.InteractiveSession()
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(tf.global_variables())
self.trainable = tf.trainable_variables()
self.rewards = []
def _assign(self):
for i in range(len(self.trainable) // 2):
assign_op = self.trainable[i + len(self.trainable) // 2].assign(
self.trainable[i])
sess.run(assign_op)
def _memorize(self, state, action, reward, new_state, dead):
self.MEMORIES.append((state, action, reward, new_state, dead))
if len(self.MEMORIES) > self.MEMORY_SIZE:
self.MEMORIES.popleft()
def _get_image(self, image):
r, g, b = image[:, :, 0], image[:, :, 1], image[:, :, 2]
gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
return imresize(gray, size=(80, 80))
def _select_action(self, state):
if np.random.rand() < self.EPSILON:
action = np.random.randint(self.OUTPUT_SIZE)
else:
action = self.get_predicted_action([state])
return action
def _construct_memories(self, replay):
states = np.array([a[0] for a in replay])
new_states = np.array([a[3] for a in replay])
Q = self.predict(states)
Q_new = self.predict(new_states)
Q_new_negative = sess.run(
self.model_negative, feed_dict={self.model_negative.X: new_states})
replay_size = len(replay)
X = np.empty((replay_size, 80, 80, 4))
Y = np.empty((replay_size, self.OUTPUT_SIZE))
for i in range(replay_size):
state_r, action_r, reward_r, new_state_r, dead_r = replay[i]
target = Q[i]
target[action_r] = reward_r
if not dead_r:
target[action_r] += self.GAMMA * Q_new_negative[
i, np.argmax(Q_new[i])]
X[i] = state_r
Y[i] = target
return X, Y
def predict(self, inputs):
return self.sess.run(self.model.logits,
feed_dict={self.model.X: inputs})
def save(self, checkpoint_name):
self.saver.save(self.sess,
os.getcwd() + "/%s.ckpt" % (checkpoint_name))
with open('%s-acc.p' % (checkpoint_name), 'wb') as fopen:
pickle.dump(self.rewards, fopen)
def load(self, checkpoint_name):
self.saver.restore(self.sess,
os.getcwd() + "/%s.ckpt" % (checkpoint_name))
with open('%s-acc.p' % (checkpoint_name), 'rb') as fopen:
self.rewards = pickle.load(fopen)
def get_predicted_action(self, sequence):
prediction = self.predict(np.array(sequence))[0]
return np.argmax(prediction)
def get_state(self):
state = self.env.getGameState()
return np.array(list(state.values()))
def get_reward(self, iterations, checkpoint):
for i in range(iterations):
total_reward = 0
self.env.reset_game()
state = self._get_image(self.env.getScreenRGB())
for k in range(self.INITIAL_IMAGES.shape[2]):
self.INITIAL_IMAGES[:, :, k] = state
dead = False
while not dead:
if (self.T_COPY + 1) % self.COPY == 0:
self._assign()
action = self._select_action(self.INITIAL_IMAGES)
real_action = 119 if action == 1 else None
reward = self.env.act(real_action)
total_reward += reward
new_state = self.get_state()
state = self._get_image(self.env.getScreenRGB())
new_state = np.append(state.reshape([80, 80, 1]),
self.INITIAL_IMAGES[:, :, :3],
axis=2)
dead = self.env.game_over()
self._memorize(self.INITIAL_IMAGES, action, reward, new_state,
dead)
batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)
replay = random.sample(self.MEMORIES, batch_size)
X, Y = self._construct_memories(replay)
cost, _ = self.sess.run([self.cost, self.optimizer],
feed_dict={
self.X: X,
self.Y: Y
})
self.INITIAL_IMAGES = new_state
self.T_COPY += 1
self.rewards.append(total_reward)
self.EPSILON = self.MIN_EPSILON + (
1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)
if (i + 1) % checkpoint == 0:
print('epoch:', i + 1, 'total rewards:', total_reward)
print('epoch:', i + 1, 'cost:', cost)
def fit(self, iterations, checkpoint):
self.get_reward(iterations, checkpoint)
class Agent:
LEARNING_RATE = 0.003
BATCH_SIZE = 32
INPUT_SIZE = 8
LAYER_SIZE = 500
OUTPUT_SIZE = 2
EPSILON = 1
DECAY_RATE = 0.005
MIN_EPSILON = 0.1
GAMMA = 0.99
INITIAL_FEATURES = np.zeros((4, INPUT_SIZE))
MEMORIES = deque()
MEMORY_SIZE = 300
COPY = 1000
T_COPY = 0
# based on documentation, features got 8 dimensions
# output is 2 dimensions, 0 = do nothing, 1 = jump
def __init__(self, screen=False, forcefps=True):
self.game = FlappyBird(pipe_gap=125)
self.env = PLE(self.game, fps=30, display_screen=screen, force_fps=forcefps)
self.env.init()
self.env.getGameState = self.game.getGameState
self.actor = Actor('actor', self.INPUT_SIZE, self.OUTPUT_SIZE, self.LAYER_SIZE)
self.actor_target = Actor('actor-target', self.INPUT_SIZE, self.OUTPUT_SIZE, self.LAYER_SIZE)
self.critic = Critic('critic', self.INPUT_SIZE, self.OUTPUT_SIZE, self.LAYER_SIZE, self.LEARNING_RATE)
self.critic_target = Critic('critic-target', self.INPUT_SIZE, self.OUTPUT_SIZE, self.LAYER_SIZE, self.LEARNING_RATE)
self.grad_critic = tf.gradients(self.critic.logits, self.critic.Y)
self.actor_critic_grad = tf.placeholder(tf.float32, [None, self.OUTPUT_SIZE])
weights_actor = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='actor')
self.grad_actor = tf.gradients(self.actor.logits, weights_actor, -self.actor_critic_grad)
grads = zip(self.grad_actor, weights_actor)
self.optimizer = tf.train.AdamOptimizer(self.LEARNING_RATE).apply_gradients(grads)
self.sess = tf.InteractiveSession()
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(tf.global_variables())
self.rewards = []
def _assign(self, from_name, to_name):
from_w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=from_name)
to_w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=to_name)
for i in range(len(from_w)):
assign_op = to_w[i].assign(from_w[i])
sess.run(assign_op)
def _memorize(self, state, action, reward, new_state, dead, rnn_state):
self.MEMORIES.append((state, action, reward, new_state, dead, rnn_state))
if len(self.MEMORIES) > self.MEMORY_SIZE:
self.MEMORIES.popleft()
def _construct_memories_and_train(self, replay):
# state_r, action_r, reward_r, new_state_r, dead_r = replay
# train actor
states = np.array([a[0] for a in replay])
new_states = np.array([a[3] for a in replay])
init_values = np.array([a[-1] for a in replay])
Q = self.sess.run(self.actor.logits, feed_dict={self.actor.X: states, self.actor.hidden_layer:init_values})
Q_target = self.sess.run(self.actor_target.logits, feed_dict={self.actor_target.X: states, self.actor_target.hidden_layer:init_values})
grads = self.sess.run(self.grad_critic, feed_dict={self.critic.X:states, self.critic.hidden_layer: init_values, self.critic.Y:Q})
self.sess.run(self.optimizer, feed_dict={self.actor.X:states, self.actor.hidden_layer:init_values, self.actor_critic_grad:grads})
# train critic
rewards = np.array([a[2] for a in replay]).reshape((-1, 1))
rewards_target = self.sess.run(self.critic_target.logits, feed_dict={self.critic_target.X:new_states, self.critic_target.hidden_layer: init_values,
self.critic_target.Y:Q_target})
for i in range(len(replay)):
if not replay[0][-1]:
rewards[i,0] += self.GAMMA * rewards_target
cost, _ = self.sess.run([self.critic.cost, self.critic.optimizer), feed_dict={self.critic.X:states, self.critic.hidden_layer: init_values,
self.critic.Y:Q, self.critic.REWARD:rewards})
return cost
def save(self, checkpoint_name):
self.saver.save(self.sess, os.getcwd() + "/%s.ckpt" %(checkpoint_name))
with open('%s-acc.p'%(checkpoint_name), 'wb') as fopen:
pickle.dump(self.rewards, fopen)
def load(self, checkpoint_name):
self.saver.restore(self.sess, os.getcwd() + "/%s.ckpt" %(checkpoint_name))
with open('%s-acc.p'%(checkpoint_name), 'rb') as fopen:
self.rewards = pickle.load(fopen)
def get_state(self):
state = self.env.getGameState()
return np.array(list(state.values()))
def get_reward(self, iterations, checkpoint):
for i in range(iterations):
total_reward = 0
self.env.reset_game()
dead = False
init_value = np.zeros((1, 2 * 512))
state = self.get_state()
for i in range(self.INITIAL_FEATURES.shape[0]):
self.INITIAL_FEATURES[i,:] = state
while not dead:
if (self.T_COPY + 1) % self.COPY == 0:
self._assign('actor', 'actor-target')
self._assign('critic', 'critic-target')
if np.random.rand() < self.EPSILON:
action = np.random.randint(self.OUTPUT_SIZE)
else:
action, last_state = sess.run(self.model.logits,
self.model.last_state,
feed_dict={self.model.X:[self.INITIAL_FEATURES],
self.model.hidden_layer:init_values})
action, init_value = np.argmax(action[0]), last_state[0]
real_action = 119 if action == 1 else None
reward = self.env.act(real_action)
total_reward += reward
new_state = np.append(self.get_state(), self.INITIAL_FEATURES[:3, :], axis = 0)
dead = self.env.game_over()
self._memorize(state, action, reward, new_state, dead, init_value)
batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)
replay = random.sample(self.MEMORIES, batch_size)
cost = self._construct_memories_and_train(replay)
self.EPSILON = self.MIN_EPSILON + (1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)
self.T_COPY += 1
self.rewards.append(total_reward)
if (i+1) % checkpoint == 0:
print('epoch:', i + 1, 'total rewards:', total_reward)
print('epoch:', i + 1, 'cost:', cost)
def fit(self, iterations, checkpoint):
self.get_reward(iterations, checkpoint)
class Agent:
LEARNING_RATE = 0.003
BATCH_SIZE = 32
INPUT_SIZE = 8
LAYER_SIZE = 500
OUTPUT_SIZE = 2
EPSILON = 1
DECAY_RATE = 0.005
MIN_EPSILON = 0.1
GAMMA = 0.99
MEMORIES = deque()
MEMORY_SIZE = 300
# based on documentation, features got 8 dimensions
# output is 2 dimensions, 0 = do nothing, 1 = jump
def __init__(self, screen=False, forcefps=True):
self.game = FlappyBird(pipe_gap=125)
self.env = PLE(self.game, fps=30, display_screen=screen, force_fps=forcefps)
self.env.init()
self.env.getGameState = self.game.getGameState
self.X = tf.placeholder(tf.float32, (None, self.INPUT_SIZE))
self.Y = tf.placeholder(tf.float32, (None, self.OUTPUT_SIZE))
input_layer = tf.Variable(tf.random_normal([self.INPUT_SIZE, self.LAYER_SIZE]))
bias = tf.Variable(tf.random_normal([self.LAYER_SIZE]))
output_layer = tf.Variable(tf.random_normal([self.LAYER_SIZE, self.OUTPUT_SIZE]))
feed_forward = tf.nn.relu(tf.matmul(self.X, input_layer) + bias)
self.logits = tf.matmul(feed_forward, output_layer)
self.cost = tf.reduce_sum(tf.square(self.Y - self.logits))
self.optimizer = tf.train.AdamOptimizer(learning_rate = self.LEARNING_RATE).minimize(self.cost)
self.sess = tf.InteractiveSession()
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(tf.global_variables())
self.rewards = []
def _memorize(self, state, action, reward, new_state, done):
self.MEMORIES.append((state, action, reward, new_state, done))
if len(self.MEMORIES) > self.MEMORY_SIZE:
self.MEMORIES.popleft()
def _select_action(self, state):
if np.random.rand() < self.EPSILON:
action = np.random.randint(self.OUTPUT_SIZE)
else:
action = self.get_predicted_action([state])
return action
def _construct_memories(self, replay):
states = np.array([a[0] for a in replay])
new_states = np.array([a[3] for a in replay])
Q = self.predict(states)
Q_new = self.predict(new_states)
replay_size = len(replay)
X = np.empty((replay_size, self.INPUT_SIZE))
Y = np.empty((replay_size, self.OUTPUT_SIZE))
for i in range(replay_size):
state_r, action_r, reward_r, new_state_r, done_r = replay[i]
target = Q[i]
target[action_r] = reward_r
if not done_r:
target[action_r] += self.GAMMA * np.amax(Q_new[i])
X[i] = state_r
Y[i] = target
return X, Y
def predict(self, inputs):
return self.sess.run(self.logits, feed_dict={self.X:inputs})
def save(self, checkpoint_name):
self.saver.save(self.sess, os.getcwd() + "/%s.ckpt" %(checkpoint_name))
with open('%s-acc.p'%(checkpoint_name), 'wb') as fopen:
pickle.dump(self.rewards, fopen)
def load(self, checkpoint_name):
self.saver.restore(self.sess, os.getcwd() + "/%s.ckpt" %(checkpoint_name))
with open('%s-acc.p'%(checkpoint_name), 'rb') as fopen:
self.rewards = pickle.load(fopen)
def get_predicted_action(self, sequence):
prediction = self.predict(np.array(sequence))[0]
return np.argmax(prediction)
def get_state(self):
state = self.env.getGameState()
return np.array(list(state.values()))
def get_reward(self, iterations, checkpoint):
for i in range(iterations):
total_reward = 0
self.env.reset_game()
done = False
while not done:
state = self.get_state()
action = self._select_action(state)
real_action = 119 if action == 1 else None
reward = self.env.act(real_action)
total_reward += reward
new_state = self.get_state()
done = self.env.game_over()
self._memorize(state, action, reward, new_state, done)
batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)
replay = random.sample(self.MEMORIES, batch_size)
X, Y = self._construct_memories(replay)
cost, _ = self.sess.run([self.cost, self.optimizer], feed_dict={self.X: X, self.Y:Y})
self.rewards.append(total_reward)
self.EPSILON = self.MIN_EPSILON + (1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)
if (i+1) % checkpoint == 0:
print('epoch:', i + 1, 'total rewards:', total_reward)
print('epoch:', i + 1, 'cost:', cost)
def fit(self, iterations, checkpoint):
self.get_reward(iterations, checkpoint)