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 run_a_game(self,game):
from ple import PLE
p = PLE(game,display_screen=True)
agent = NaiveAgent(p.getActionSet())
p.init()
reward = p.act(p.NOOP)
for i in range(NUM_STEPS):
obs = p.getScreenRGB()
reward = p.act(agent.pickAction(reward,obs))
def main_naive():
game = FlappyBird()
env = PLE(game, fps=30, display_screen=True)
my_agent = naive.NaiveAgent(allowed_actions=env.getActionSet())
env.init()
reward = 0.0
nb_frames = 10000
for i in range(nb_frames):
if env.game_over():
env.reset_game()
observation = env.getScreenRGB()
action = my_agent.pickAction(reward, observation)
reward = env.act(action)
class PLECatcherEnv(gym.Env):
metadata = {'render.modes': ['human', 'rgb_array']}
def __init__(self, game_name='Catcher', display_screen=True, ple_game=True, obs_type="Image", reward_type = 1):
'''
For Catcher:
getGameState() returns [player x position, player velocity, fruits x position, fruits y position]
@Params:
obs_type :
"RAM" : getGameState()
"Image" : (64, 64, 3)
reward_type :
0 : means [reward1, reward2]
1 : means raw reward
2 : means change of x-axis distance from fruit
'''
# set headless mode
os.environ['SDL_VIDEODRIVER'] = 'dummy'
# open up a game state to communicate with emulator
import importlib
if ple_game:
game_module_name = ('ple.games.%s' % game_name).lower()
else:
game_module_name = game_name.lower()
game_module = importlib.import_module(game_module_name)
game = getattr(game_module, game_name)()
##################################################################
# old one
#self.game_state = PLE(game, fps=30, display_screen=display_screen)
# use arg state_preprocessor to support self.game_state.getGameState()
self.game_state = PLE(game, fps=30, display_screen=display_screen, state_preprocessor = self.process_state)
##################################################################
self.game_state.init()
self._action_set = self.game_state.getActionSet()
self.action_space = spaces.Discrete(len(self._action_set))
self.screen_height, self.screen_width = self.game_state.getScreenDims()
self.observation_space = spaces.Box(low=0, high=255, shape=(self.screen_width, self.screen_height, 3), dtype = np.uint8)
self.viewer = None
############################################
self.obs_type = obs_type
self.reward_type = reward_type
# every reward type's max-abs value
self.rewards_ths = [1.0, 2.0]
# change observation space:
self.img_width = 84
self.img_height = 84
self.img_shape = (self.img_width, self.img_height, 3)
if self.obs_type == "Image":
self.observation_space = spaces.Box(low = 0, high = 255, shape = self.img_shape, dtype = np.uint8)
elif self.obs_type == "RAM":
self.observation_space = spaces.Box(low = -100.0, high = 100.0, shape = (4, ), dtype = np.float32)
############################################
#############################################
# Add state processer
def process_state(self, state):
return np.array([state.values()])
#############################################
def _step(self, a, gamma = 0.99):
#############################################
if isinstance(a,np.ndarray):
a = a[0]
# old observation
old_ram = self.game_state.getGameState()
#############################################
reward = self.game_state.act(self._action_set[a])
#############################################
#state = self._get_image()
if self.obs_type == "Image":
state = self._get_image()
#############################################
terminal = self.game_state.game_over()
#############################################
# new observation
ram = self.game_state.getGameState()
#############################################
#############################################
if self.reward_type == 1:
reward = reward / self.rewards_ths[0]
# reward 2
if self.reward_type == 2:
reward = self.get_reward(reward, old_ram, ram, terminal, 2, gamma)
# reward 0
if self.reward_type == 0:
reward1 = reward / self.rewards_ths[0]
reward2 = self.get_reward(reward, old_ram, ram, terminal, 2, gamma)
reward = np.array([reward1, reward2])
##############################################
############################################################
'''
# reward scaling
if self.reward_type == 0:
for rt in range(len(reward)):
reward[rt] = reward[rt] / self.rewards_ths[rt]
else:
reward = reward / self.rewards_ths[self.reward_type - 1]
'''
############################################################
##############################################
# obs
if self.obs_type == "RAM":
state = self.game_state.getGameState()
state = np.array(list(state[0]))
##############################################
return state, reward, terminal, {}
#############################################
# Add for reward
#############################################
def get_reward(self, src_reward, old_ram, ram, done, reward_type, gamma):
'''
@Params:
old_ram, ram : numpy.array, [dict_values([x, y, z, w])]
reward_type : 2 , distance of x-axis change
'''
old_ram = list(old_ram[0])
ram = list(ram[0])
reward = src_reward
if not done:
if reward_type == 2:
old_px, old_fx = old_ram[0], old_ram[2]
px, fx = ram[0], ram[2]
old_dis = abs(old_px - old_fx)
dis = abs(px - fx)
reward = old_dis - gamma * dis
# a new epoch
old_fy, fy = old_ram[3], ram[3]
if old_fy > fy:
reward = 0.0
reward = min(reward, 2.0)
reward = max(reward, -2.0)
reward = src_reward / self.rewards_ths[0] + reward / self.rewards_ths[1]
return reward
#############################################
#############################################
def _get_image(self):
image_rotated = np.fliplr(np.rot90(self.game_state.getScreenRGB(),3)) # Hack to fix the rotated image returned by ple
##########################################
# resize image
img = Image.fromarray(image_rotated)
img = img.resize((self.img_width, self.img_height), Image.ANTIALIAS)
image_resized = np.array(img).astype(np.uint8)
##########################################
return image_resized
@property
def _n_actions(self):
return len(self._action_set)
# return: (states, observations)
def _reset(self):
self.observation_space = spaces.Box(low=0, high=255, shape=(self.screen_width, self.screen_height, 3), dtype = np.uint8)
self.game_state.reset_game()
#######################################
if self.obs_type == "Image":
state = self._get_image()
elif self.obs_type == "RAM":
state = self.game_state.getGameState()
state = np.array(list(state[0]))
#######################################
return state
def _render(self, mode='human', close=False):
if close:
if self.viewer is not None:
self.viewer.close()
self.viewer = None
return
img = self._get_image()
if mode == 'rgb_array':
return img
elif mode == 'human':
from gym.envs.classic_control import rendering
if self.viewer is None:
self.viewer = rendering.SimpleImageViewer()
self.viewer.imshow(img)
def _seed(self, seed):
rng = np.random.RandomState(seed)
self.game_state.rng = rng
self.game_state.game.rng = self.game_state.rng
self.game_state.init()
display_screen = True
reward = 0.0
max_noops = 20
nb_frames = 15000
#make a PLE instance.
p = PLE(game,
fps=fps,
frame_skip=frame_skip,
num_steps=num_steps,
force_fps=force_fps,
display_screen=display_screen)
#our Naive agent!
agent = NaiveAgent(p.getActionSet())
#init agent and game.
p.init()
#lets do a random number of NOOP's
for i in range(np.random.randint(0, max_noops)):
reward = p.act(p.NOOP)
#start our training loop
for f in range(nb_frames):
#if the game is over
if p.game_over():
p.reset_game()
obs = p.getScreenRGB()
actionIndex = -1
actionIndex0 = -1
else:
actionIndex0=softMax(np.abs(output))#np.argmax(output)+1
print(actionIndex0)
if actionIndex0==0:
if np.sign(actionIndex0)==1:
actionIndex = 0
else:
actionIndex = 3
elif actionIndex0==1:
if np.sign(actionIndex0)==1:
actionIndex = 1
else:
actionIndex = 2
action = p.getActionSet()[actionIndex]#ulrdn
#[115, 100, 119, 97, None]
# myAgent.pickAction(reward, obs)
reward = p.act(action)
print('reward: %f'%(reward))
#points += (reward - rewardLast)-0.0001*dt*points
points = (10+reward)/10+(reward - rewardLast) if reward>-10 else reward - rewardLast
print('points: %f'%(points))
ModulatorAmount = points
if ModulatorAmount<0:
ModulatorAmount=0
ADSA.StepSynapseDynamics(dt, ModulatorAmount)
rewardLast=reward
NeuonNumber=1
newSlice= [slice(None)]*3
from ple.games.waterworld import WaterWorld
# lets adjust the rewards our agent recieves
rewards = {
"tick":
-0.01, # each time the game steps forward in time the agent gets -0.1
"positive": 1.0, # each time the agent collects a green circle
"negative": -5.0, # each time the agent bumps into a red circle
}
# make a PLE instance.
# use lower fps so we can see whats happening a little easier
game = WaterWorld(width=256, height=256, num_creeps=8)
p = PLE(game,
fps=15,
force_fps=False,
display_screen=True,
reward_values=rewards)
# we pass in the rewards and PLE will adjust the game for us
p.init()
actions = p.getActionSet()
for i in range(1000):
if p.game_over():
p.reset_game()
action = actions[np.random.randint(0, len(actions))] # random actions
reward = p.act(action)
print "Score: {:0.3f} | Reward: {:0.3f} ".format(p.score(), reward)
def train_agent(number_of_episodes):
game = FlappyBird()
rewards = {
"positive": 1.0,
"negative": 0.0,
"tick": 0.0,
"loss": -5.0,
"win": 0.0
}
env = PLE(game=game, fps=30, display_screen=False, reward_values=rewards)
# Reset environment at the beginning
env.reset_game()
training_score = 0
max_training_score = 0
episode_number = 1
state_action_reward = ()
every_100th = 1
results = []
while number_of_episodes > 0:
if episode_number == 50000:
f = open("monte_50000.txt", "w")
f.write(str(monte_carlo_q_agent.Q_matrix))
f.close()
f = open("results_50000.txt", "w")
f.write(str(results))
f.close()
# Get current state
state = MonteCarloQLearningAgent.get_state(env.game.getGameState())
# Select action in state "state"
action = monte_carlo_q_agent.compute_action_from_q_values(state)
if action is None:
raise IllegalActionException("Illegal action occurred.")
"""
After choosing action, get reward.
PLE environment method act() returns the reward that the agent has accumulated while performing the action.
"""
reward = env.act(env.getActionSet()[action])
training_score += reward
max_training_score = max(training_score, max_training_score)
game_over = env.game_over()
# observe the result
if state_action_reward:
monte_carlo_q_agent.update(state_action_reward[0],
state_action_reward[1], state,
state_action_reward[2])
state_action_reward = (state, action, reward)
if game_over:
print("===========================")
print("Episode: " + str(episode_number))
print("Training score: " + str(training_score))
print("Max. training score: " + str(max_training_score))
print("===========================\n")
if every_100th == 100:
results.append((episode_number, training_score))
every_100th = 0
episode_number += 1
number_of_episodes -= 1
training_score = 0
env.reset_game()
import numpy as np
from ple import PLE
from ple.games.snake import Snake
agent = Snake(width=256, height=256)
env = PLE(agent, fps=15, force_fps=False, display_screen=True)
env.init()
actions = env.getActionSet()
q_states = {((1, 1), 0): 0}
count_q = {(0, 0): 0}
w = np.random.rand(4)
alpha = 0.5
gama = 1
epsilon = 0.7
steps = 1
# checked: correct :)
def compute_sprim(state, action):
new_state = (0, 0)
if action == 119:
new_state = (state[0], state[1] + 1)
if action == 97:
new_state = (state[0] + 1, state[1])
if action == 100:
new_state = (state[0] - 1, state[1])
if action == 115:
class CustomGameEnv(gym.Env):
def __init__(self, task={}):
self._task = task
os.environ['SDL_VIDEODRIVER'] = 'dummy'
import importlib
game_module = importlib.import_module('ple.games.customgame')
game = getattr(game_module, 'customgame')()
self.game_state = PLE(game, fps=30, display_screen=False)
self._action_set = self.game_state.getActionSet()
self.action_space = spaces.Discrete(len(self._action_set))
self.screen_width, self.screen_height = self.game_state.getScreenDims()
self.observation_space = spaces.Box(low=0,
high=255,
shape=(self.screen_width,
self.screen_height, 3))
self.num_actions = len(self._action_set)
self.viewer = None
self.reward_mult = 1.0
def seed(self, seed=None):
if not seed:
seed = np.random.randint(2**31 - 1)
rng = np.random.RandomState(seed)
self.game_state.rng = rng
self.game_state.game.rng = self.game_state.rng
self.game_state.init()
return [seed]
def reset_task(self, task):
pass
def render(self, mode='human'):
img = self._get_image()
if mode == 'rgb_array':
return img
elif mode == 'human':
from gym.envs.classic_control import rendering
if self.viewer is None:
self.viewer = rendering.SimpleImageViewer()
self.viewer.imshow(img)
def reset(self):
self.observation_space = spaces.Box(low=0,
high=255,
shape=(self.screen_width,
self.screen_height, 3))
self.game_state.reset_game()
state = self._get_image()
return state
def _get_image(self):
image_rotated = np.fliplr(
np.rot90(self.game_state.getScreenRGB(),
3)) # Hack to fix the rotated image returned by ple
return image_rotated
def step(self, action):
reward = self.reward_mult * self.game_state.act(
self._action_set[action])
state = self._get_image()
terminal = self.game_state.game_over()
return state, reward, terminal, {}
r = p.act(self.action_set[action])
if r == 0:
r = 1
if r == 1:
r = 10
else:
r = -1000
return r
if __name__ == "__main__":
episodes = 2000_000000
game = FlappyBird()
p = PLE(game, fps=30, display_screen=False)
p.init()
agent = Agent(p.getActionSet())
max_score = 0
for episode in range(episodes):
p.reset_game()
state = agent.get_state(game.getGameState())
agent.update_greedy()
while True:
action = agent.get_best_action(state)
reward = agent.act(p, action)
next_state = agent.get_state(game.getGameState())
agent.update_q_table(state, action, next_state, reward)
current_score = p.score()
state = next_state
if p.game_over():
max_score = max(current_score, max_score)
# create our game
force_fps = True # slower speed
display_screen = True
state_preprocessor = nv_state_preprocessor
reward = 0.0
game = WaterWorld()
# make a PLE instance.
p = PLE(game,
force_fps=force_fps,
display_screen=display_screen,
state_preprocessor=state_preprocessor)
# our Naive agent!
agent = SmartAgent(actions=p.getActionSet())
# init agent and game.
p.init()
# start our loop
score = 0.0
for i in range(10):
# if the game is over
if p.game_over():
p.reset_game()
while p.game_over() == False:
obs = p.getGameState()
action = agent.pickAction(reward, obs)
reward = p.act(action) # reward after an action
score = game.getScore()
class FlappyBirdGame():
def __init__(self,
reward_values={},
reward_discount=0.99,
pip_gap=100,
display_screen=True,
fps=30,
force_fps=True):
self.game = PLE(FlappyBird(pipe_gap=pip_gap),
reward_values=reward_values,
fps=fps,
force_fps=force_fps,
display_screen=display_screen)
self.game.init()
self.actions = self.game.getActionSet()
self.reward_discount = reward_discount
@staticmethod
def random_agent(*args, **kwargs):
return torch.rand(1)
def calculate_trial_reward(self, rewards_tensor):
rewards_output = torch.empty(rewards_tensor.shape[0])
for i in range(rewards_tensor.shape[0]):
discount_vector = torch.Tensor([self.reward_discount] *
(rewards_tensor.shape[0] - i))
pv_rewards = sum(rewards_tensor[i:] *
discount_vector**torch.FloatTensor(
range(rewards_tensor.shape[0] - i)))
rewards_output[i] = pv_rewards
rewards_output = rewards_output.reshape((-1, 1))
return rewards_output
@staticmethod
def observation_to_torch_tensor(observation):
obs_tensor = torch.FloatTensor([
observation['player_y'], observation['player_vel'],
observation['next_pipe_dist_to_player'],
observation['next_pipe_top_y'], observation['next_pipe_bottom_y'],
observation['next_next_pipe_dist_to_player'],
observation['next_next_pipe_top_y'],
observation['next_next_pipe_bottom_y']
])
obs_tensor = obs_tensor.reshape((1, 8))
return obs_tensor
def run_trial(self, agent=None, sample=True, verbose=False):
if agent is None:
agent = self.random_agent
if self.game.game_over():
self.game.reset_game()
rewards = torch.empty(0)
observations = torch.empty((0, 8))
agent_decisions = torch.empty((0, 1))
actual_decisions = torch.empty((0, 1))
while not self.game.game_over():
observation = self.observation_to_torch_tensor(
self.game.getGameState())
agent_decision = agent(observation)
if sample:
actual_decision = torch.bernoulli(agent_decision)
else:
actual_decision = torch.FloatTensor(
[1]) if agent_decision > 0.5 else torch.FloatTensor([0])
actual_decision = actual_decision.reshape((1, 1))
agent_decision = agent_decision.reshape((1, 1))
if actual_decision == 1:
action = self.actions[1]
else:
action = self.actions[0]
reward = torch.FloatTensor([self.game.act(action)])
# reward shaping
# if (observation[0][0] < observation[0][4]) and (observation[0][0] > observation[0][3]):
# reward = torch.add(reward, torch.tensor(0.2))
# else:
# reward = torch.add(reward, torch.tensor(-0.2))
rewards = torch.cat((rewards, reward))
observations = torch.cat((observations, observation))
agent_decisions = torch.cat((agent_decisions, agent_decision))
actual_decisions = torch.cat((actual_decisions, actual_decision))
if verbose:
print(f'action: {action}')
print(f'observation: {observation}')
print(f'reward: {reward}')
return {
'observations': observations,
'rewards': self.calculate_trial_reward(rewards),
'agent_decisions': agent_decisions,
'actual_decisions': actual_decisions
}
def run_n_trials(self, n_trials, agent=None, sample=True):
out_results = {
'observations': torch.empty(0),
'rewards': torch.empty(0),
'agent_decisions': torch.empty(0),
'actual_decisions': torch.empty(0)
}
for i in range(n_trials):
results = self.run_trial(agent, sample)
out_results['observations'] = torch.cat(
(out_results['observations'], results['observations']))
out_results['rewards'] = torch.cat(
(out_results['rewards'], results['rewards']))
out_results['agent_decisions'] = torch.cat(
(out_results['agent_decisions'], results['agent_decisions']))
out_results['actual_decisions'] = torch.cat(
(out_results['actual_decisions'], results['actual_decisions']))
return out_results
return self.qLearning.getAction(state)
def incorporateFeedback(self, state, action, reward, newState):
self.qLearning.incorporateFeedback(state, action, reward, newState)
def printWeights(self):
print str(self.qLearning.getWeights())
print 'num weights: %d' % len(self.qLearning.getWeights())
############################################################
if __name__ == '__main__':
game = Pixelcopter(width=200, height=200)
env = PLE(game, fps=30, display_screen=displayScreen)
agent = Bot(actions=env.getActionSet())
env.init()
total_reward = 0.0
min_reward = float('inf')
max_reward = float('-inf')
all_episode_scores = []
plot_episode_scores = []
plotted_episodes = []
for i in range(num_runs): # should run until qvalues converge
episode_reward = 0.0
frames = []
while not env.game_over():
state = game.getGameState()
obs = list(ple_env.getGameState().values())
episode_reward += reward
# if render:
# ple_env.getScreenRGB()
if ple_env.game_over():
break
eval_reward.append(episode_reward)
return np.mean(eval_reward)
# 创建环境
game = Pong(cpu_speed_ratio=0.3)
# game = Pong()
pong = PLE(game, display_screen=True, force_fps=True)
# 根据parl框架构建agent
print(pong.getActionSet())
action_dim = len(pong.getActionSet())
obs_shape = len(pong.getGameState())
print(pong.getGameState())
# 创建经验池
rpm = ReplayMemory(MEMORY_SIZE) # DQN的经验回放池
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
class PLEEnv(gym.Env):
metadata = {'render.modes': ['human', 'rgb_array']}
def __init__(self,
prespecified_game=True,
game_name='MyCatcher',
display_screen=True,
rgb_state=False):
# open up a game state to communicate with emulator
import importlib
if prespecified_game:
game_module_name = ('ple.games.%s' % game_name).lower()
else:
game_module_name = ('domains.ple.%s' % game_name).lower()
game_module = importlib.import_module(game_module_name)
self.game = getattr(game_module, game_name)()
self.rgb_state = rgb_state
if self.rgb_state:
self.game_state = PLE(self.game,
fps=30,
display_screen=display_screen)
else:
if prespecified_game:
self.game_state = PLE(
self.game,
fps=30,
display_screen=display_screen,
state_preprocessor=process_state_prespecified)
else:
self.game_state = PLE(self.game,
fps=30,
display_screen=display_screen,
state_preprocessor=process_state)
self.game_state.init()
self._action_set = self.game_state.getActionSet()
self.action_space = spaces.Discrete(len(self._action_set))
if self.rgb_state:
self.state_width, self.state_height = self.game_state.getScreenDims(
)
self.observation_space = spaces.Box(low=0,
high=255,
shape=(self.state_width,
self.state_height, 3))
else:
self.state_dim = self.game_state.getGameStateDims()
self.observation_space = spaces.Box(low=0,
high=255,
shape=self.state_dim)
self.viewer = None
self.feature_bins = []
if hasattr(self.game, 'feature_bins'):
self.feature_bins = self.game.feature_bins
def get_source_state(self, state):
if hasattr(self.game, 'get_source_state'):
return self.game.get_source_state(state)
return None
def get_uniform_state_weights(self):
if hasattr(self.game, 'get_uniform_state_weights'):
return self.game.get_uniform_state_weights()
else:
states = self.get_states()
weights = np.ones(len(states))
weights = [float(i) / sum(weights) for i in weights]
return states, weights
def generate_training_subset(self, percent_sim_data):
if hasattr(self.game, 'generate_training_subset'):
return self.game.generate_training_subset(percent_sim_data)
def set_to_training_set(self):
if hasattr(self.game, 'set_to_training_set'):
return self.game.set_to_training_set()
def set_to_testing_set(self):
if hasattr(self.game, 'set_to_testing_set'):
return self.game.set_to_testing_set()
def get_states(self):
if hasattr(self.game, 'states'):
return self.game.states
def _step(self, a):
reward = self.game_state.act(self._action_set[a])
state = self._get_state()
terminal = self.game_state.game_over()
return state, reward, terminal, {}
def _get_image(self, game_state):
image_rotated = np.fliplr(
np.rot90(game_state.getScreenRGB(),
3)) # Hack to fix the rotated image returned by ple
return image_rotated
def _get_state(self):
if self.rgb_state:
return self._get_image(self.game_state)
else:
return self.game_state.getGameState()
@property
def _n_actions(self):
return len(self._action_set)
# return: (states, observations)
def _reset(self):
if self.rgb_state:
self.observation_space = spaces.Box(low=0,
high=255,
shape=(self.state_width,
self.state_height, 3))
else:
self.observation_space = spaces.Box(low=0,
high=255,
shape=self.state_dim)
self.game_state.reset_game()
state = self._get_state()
return state
def _render(self, mode='human', close=False):
if close:
if self.viewer is not None:
self.viewer.close()
self.viewer = None
return
img = self._get_image(self.game_state)
if mode == 'rgb_array':
return img
elif mode == 'human':
from gym.envs.classic_control import rendering
if self.viewer is None:
self.viewer = rendering.SimpleImageViewer()
self.viewer.imshow(img)
def _seed(self, seed):
rng = np.random.RandomState(seed)
self.game_state.rng = rng
self.game_state.game.rng = self.game_state.rng
self.game_state.init()
import numpy as np import pygame from pygame.locals import * class TestAgent(): def __init__(self, actions): self.actions = actions def doAction(self,reward,obs): #print 'hello' for event in pygame.event.get(): if event.type == KEYDOWN: return self.actions[0] return None game = RunningMinion() #game = WaterWorld() p = PLE(game, fps=30, display_screen=True) agent = TestAgent(p.getActionSet()) p.init() reward = 0.0 nb_frames = 2000 for i in range(nb_frames): if p.game_over(): p.reset_game() if i%1==0: obser = p.getScreenRGB() action = agent.doAction(reward,obser) reward = p.act(action)
rewards = {
"tick": -0.1, # each time the game steps forward in time the agent gets -0.1
"positive": 1, # each time the agent collects a green circle
"negative": -5.0, # each time the agent bumps into a red circle
}
# make a PLE instance.
# use lower fps so we can see whats happening a little easier
game = WaterWorld(width=100, height=100, num_creeps=15)
# p = PLE(game, reward_values=rewards)
p = PLE(game, fps=30, force_fps=False, display_screen=True,
reward_values=rewards)
p.init()
actions = p.getActionSet()[:-1]
agent = Agent(len(actions))
epochs = 10000000
game_duration = 1000
for epoch in range(epochs):
p.reset_game()
for it in range(1000):
if p.game_over():
p.reset_game()
print "Finished with score:" + str(p.score())
current_state = np.array(p.getScreenGrayscale()).reshape((10000, ))
action = agent.act(np.array([current_state]))
return self.actions[1]
elif fwd[1] < 0 and abs(fwd[1]) > abs(fwd[0]):
return self.actions[2]
elif fwd[0] < 0 and abs(fwd[0]) > abs(fwd[1]):
return self.actions[3]
else:
return self.actions[4]
os.putenv('SDL_VIDEODRIVER', 'fbcon')
os.environ["SDL_VIDEODRIVER"] = "dummy"
# create our game
force_fps = True # slower speed
display_screen = False
game = WaterWorld()
# make a PLE instance.
p = PLE(game,force_fps=force_fps)
# init agent and game.
p.init()
p.display_screen = True
reward = 0
agent = MyAgent(p.getActionSet())
while p.game_over() == False:
state = p.getGameState()
action = agent.pickAction(reward, state)
reward = p.act(action)
print p.score()
catcher_dict['state_stds'] = [
13.89457683, 2.04087944, 17.41686248, 23.38546788
]
game_params = {'cartpole': cartpole_dict, 'catcher': catcher_dict}
if __name__ == "__main__":
# Initiate cartpole envs
cartpole_env = gym.make('CartPole-v1')
# Initiate catcher envs
catcher_env = PLE(Catcher(init_lives=1),
state_preprocessor=process_state,
display_screen=False)
catcher_env.init()
game_params['catcher']['actions'] = catcher_env.getActionSet()
envs = {'cartpole': cartpole_env, 'catcher': catcher_env}
# Initialise the first task: cartpole
curr_task = sim_params['first_task']
env = envs[curr_task]
# Multiple replay databases maintained if multitasking
if train_params['multitask']:
mem_length = train_params['replay_sizes']
else:
mem_length = game_params[curr_task]['memory_size']
# Create agent
reward = ple_env.act(action)
obs = list(ple_env.getGameState().values())
episode_reward += reward
if render:
ple_env.getScreenRGB()
if ple_env.game_over():
break
eval_reward.append(episode_reward)
return np.mean(eval_reward)
# 创建环境
game = FlappyBird()
p = PLE(game, fps=30, display_screen=True, force_fps=True)
act_dim = len(p.getActionSet())
states = len(p.getGameState())
rpm = ReplayMemory(MEMORY_SIZE)
model = Model(act_dim=act_dim)
alg = DQN(model, act_dim=act_dim, gamma=GAMMA, lr=LEARNING_RATE)
agent = Agent(alg,
obs_dim=states,
act_dim=act_dim,
e_greed_decrement=0,
e_greed=0) # e_greed有一定概率随机选取动作，探索
# 加载模型
# if os.path.exists('./model.ckpt'):
# agent.restore('./model.ckpt')
game = FlappyBird()
p = PLE(game,
fps=30,
display_screen=True,
force_fps=False,
state_preprocessor=process_state)
p.init()
game.ple = p
p.init()
#print(p.getActionSet())
#agent
action_set = p.getActionSet()
agent = RandomSearch(len(action_set), p.getGameStateDims()[1])
# agent.load("flappy1_100.h5")
nb_games = 1
nb_frames = 0
flag_game_10 = False
flag_game_100 = False
flag_game_50 = False
score_game = 0
last_50_games_score = deque(maxlen=50)
EXPLORE = 5000000 #small is 300000, big is 5000000
FINAL_EPSILON = 0.0001
class PygameLearningEnvironment(Environment):
def __init__(self, game_name, rewards, state_as_image = True, fps = 30, force_fps=True, frame_skip=2,
hold_action=2, visualize=False, width=84, height=84, lives=1):
"""
Initialize Pygame Learning Environment
https://github.com/ntasfi/PyGame-Learning-Environment
Args:
env_name: PLE environment
fps: frames per second
force_fps: False for slower speeds
frame_skip: number of env frames to skip
hold_action: number of env frames to hold each action for
isRGB: get color or greyscale version of statespace #isRGB = False,
game_height,game_width: height and width of environment
visualize: If set True, the program will visualize the trainings, will slow down training
lives: number of lives in game. Game resets on game over (ie lives = 0). only in Catcher and Pong (score)
"""
self.env_name = game_name
self.rewards = rewards
self.lives = lives
self.state_as_image = state_as_image
self.fps = fps #30 # frames per second
self.force_fps = force_fps #True # False for slower speeds
self.frame_skip = frame_skip # frames to skip
self.ple_num_steps = hold_action # frames to continue action for
#self.isRGB = isRGB #always returns color, lets tensorforce due the processing
self.visualize = visualize
self.width = width
self.height = height
#testing
self.reached_terminal = 0
self.episode_time_steps = 0
self.episode_reward = 0
self.total_time_steps = 0
if self.env_name == 'catcher':
self.game = Catcher(width=self.width, height=self.height,init_lives=self.lives)
elif self.env_name == 'pixelcopter':
self.game = Pixelcopter(width=self.width, height=self.height)
elif self.env_name == 'pong':
self.game = Pong(width=self.width, height=self.height,MAX_SCORE=self.lives)
elif self.env_name == 'puckworld':
self.game = PuckWorld(width=self.width, height=self.height)
elif self.env_name == 'raycastmaze':
self.game = RaycastMaze(width=self.width, height=self.height)
elif self.env_name == 'snake':
self.game = Snake(width=self.width, height=self.height)
elif self.env_name == 'waterworld':
self.game = WaterWorld(width=self.width, height=self.height)
elif self.env_name == 'monsterkong':
self.game = MonsterKong()
elif self.env_name == 'flappybird':
self.game = FlappyBird(width=144, height=256) # limitations on height and width for flappy bird
else:
raise TensorForceError('Unknown Game Environement.')
if self.state_as_image:
process_state = None
else:
#create a preprocessor to read the state dictionary as a numpy array
def process_state(state):
# ret_value = np.fromiter(state.values(),dtype=float,count=len(state))
ret_value = np.array(list(state.values()), dtype=np.float32)
return ret_value
# make a PLE instance
self.env = PLE(self.game,reward_values=self.rewards,fps=self.fps, frame_skip=self.frame_skip,
num_steps=self.ple_num_steps,force_fps=self.force_fps,display_screen=self.visualize,
state_preprocessor = process_state)
#self.env.init()
#self.env.act(self.env.NOOP) #game starts on black screen
#self.env.reset_game()
#self.env.act(self.env.NOOP)
#self.env.act(self.env.NOOP)
#self.env.act(self.env.NOOP)
#self.env.act(self.env.NOOP)
#self.env.reset_game()
# setup gamescreen object
if state_as_image:
w, h = self.env.getScreenDims()
self.gamescreen = np.empty((h, w, 3), dtype=np.uint8)
else:
self.gamescreen = np.empty(self.env.getGameStateDims(), dtype=np.float32)
# if isRGB:
# self.gamescreen = np.empty((h, w, 3), dtype=np.uint8)
# else:
# self.gamescreen = np.empty((h, w), dtype=np.uint8)
# setup action converter
# PLE returns legal action indexes, convert these to just numbers
self.action_list = self.env.getActionSet()
self.action_list = sorted(self.action_list, key=lambda x: (x is None, x))
def __str__(self):
return 'PygameLearningEnvironment({})'.format(self.env_name)
def close(self):
pygame.quit()
self.env = None
def reset(self):
# if isinstance(self.gym, gym.wrappers.Monitor):
# self.gym.stats_recorder.done = True
#env.act(env.NOOP) # need to take an action or screen is black
# clear gamescreen
if self.state_as_image:
self.gamescreen = np.empty(self.gamescreen.shape, dtype=np.uint8)
else:
self.gamescreen = np.empty(self.gamescreen.shape, dtype=np.float32)
self.env.reset_game()
return self.current_state
def execute(self, actions):
#print("lives check in ple {}".format(self.env.lives()))
#self.env.saveScreen("test_screen_capture_before_{}.png".format(self.total_time_steps))
#lives_check = self.env.lives() #testing code
ple_actions = self.action_list[actions]
reward = self.env.act(ple_actions)
state = self.current_state
# testing code
# self.env.saveScreen("test_screen_capture_after_{}.png".format(self.total_time_steps))
# self.episode_time_steps += 1
# self.episode_reward += reward
# self.total_time_steps += 1
# print("reward is {}".format(reward))
# #if self.env.lives() != lives_check:
# # print('lives are different is game over? {}'.format(self.env.game_over()))
# print('lives {}, game over {}, old lives {}'.format(self.env.lives(),self.env.game_over(),lives_check))
if self.env.game_over():
terminal = True
# testing code
self.reached_terminal += 1
# print("GAME OVER reached terminal {}".format(self.reached_terminal))
# print("episode time steps {}, episode reward {}".format(self.episode_time_steps,self.episode_reward))
# self.episode_reward = 0
# self.episode_time_steps = 0
# print("total timesteps {}".format(self.total_time_steps))
else:
terminal = False
return state, terminal, reward
@property
def actions(self):
return dict(type='int', num_actions=len(self.action_list), names=self.action_list)
# @property
# def actions(self):
# return OpenAIGym.action_from_space(space=self.gym.action_space)
#ALE implementation
# @property
# def actions(self):
# return dict(type='int', num_actions=len(self.action_inds), names=self.action_names)
@property
def states(self):
return dict(shape=self.gamescreen.shape, type=float)
@property
def current_state(self):
#returned state can either be an image or an np array of key components
if self.state_as_image:
self.gamescreen = self.env.getScreenRGB()
# if isRGB:
# self.gamescreen = self.env.getScreenRGB()
# else:
# self.gamescreen = self.env.getScreenGrayscale()
else:
self.gamescreen = self.env.getGameState()
return np.copy(self.gamescreen)
#ALE implementation
# @property
# def states(self):
# return dict(shape=self.gamescreen.shape, type=float)
# @property
# def current_state(self):
# self.gamescreen = self.ale.getScreenRGB(self.gamescreen)
# return np.copy(self.gamescreen)
# @property
# def is_terminal(self):
# if self.loss_of_life_termination and self.life_lost:
# return True
# else:
# return self.ale.game_over()
class PLEWaterWorldEnv(gym.Env):
metadata = {'render.modes': ['human', 'rgb_array']}
def __init__(self,
game_name='WaterWorld',
display_screen=True,
ple_game=True,
obs_type="Image",
reward_type=1):
'''
For WaterWorld:
getGameState() returns [player x position, player y position, player x velocity, player y velocity, player distance to each creep]
player distance to each creep is a dict with "GOOD" : [], "BAD" : []
@Params:
obs_type :
"RAM" : getGameState()
"Image" : (48, 48, 3)
reward_type :
0 : means [reward1, reward2]
1 : means raw reward
2 : means change of dis = sum(distance_from_good) - sum(distance_from_bad)
'''
# set headless mode
os.environ['SDL_VIDEODRIVER'] = 'dummy'
# open up a game state to communicate with emulator
import importlib
if ple_game:
game_module_name = ('ple.games.%s' % game_name).lower()
else:
game_module_name = game_name.lower()
game_module = importlib.import_module(game_module_name)
game = getattr(game_module, game_name)()
##################################################################
# old one
#self.game_state = PLE(game, fps=30, display_screen=display_screen)
# use arg state_preprocessor to support self.game_state.getGameState()
self.game_state = PLE(game,
fps=30,
display_screen=display_screen,
state_preprocessor=self.process_state)
##################################################################
self.game_state.init()
self._action_set = self.game_state.getActionSet()
self.action_space = spaces.Discrete(len(self._action_set))
self.screen_height, self.screen_width = self.game_state.getScreenDims()
self.observation_space = spaces.Box(low=0,
high=255,
shape=(self.screen_width,
self.screen_height, 3),
dtype=np.uint8)
self.viewer = None
############################################
self.obs_type = obs_type
self.reward_type = reward_type
# every reward type's max-abs value
self.rewards_ths = [10.0, 5.0]
# change observation space:
self.img_width = 84
self.img_height = 84
self.img_shape = (self.img_width, self.img_height, 3)
if self.obs_type == "Image":
self.observation_space = spaces.Box(low=0,
high=255,
shape=self.img_shape,
dtype=np.uint8)
else:
print("Water world only supports image observation!")
sys.exit(0)
############################################
#############################################
# Add state processer
def process_state(self, state):
return np.array([state.values()])
#############################################
def _step(self, a, gamma=0.99):
#############################################
# old observation
old_ram = self.game_state.getGameState()
#############################################
reward = self.game_state.act(self._action_set[a])
state = self._get_image()
terminal = self.game_state.game_over()
#############################################
# new observation
ram = self.game_state.getGameState()
#############################################
#############################################
# reward 2
if self.reward_type == 2:
reward = self.get_reward(old_ram, ram, terminal, 2, gamma)
# reward 0
if self.reward_type == 0:
reward1 = reward
reward2 = self.get_reward(old_ram, ram, terminal, 2, gamma)
reward = np.array([reward1, reward2])
##############################################
############################################################
# reward scaling
if self.reward_type == 0:
for rt in range(len(reward)):
reward[rt] = reward[rt] / self.rewards_ths[rt]
else:
reward = reward / self.rewards_ths[self.reward_type - 1]
############################################################
return state, reward, terminal, {}
#############################################
# Add for reward
#############################################
def get_reward(self, old_ram, ram, done, reward_type, gamma=0.99):
'''
@Params:
old_ram, ram : numpy.array, [dict_values([x, y, z, w, {"GOOD" : [], "BAD" : []}])]
reward_type : 2 , change of distance from good - bad
'''
old_ram = list(old_ram[0])
ram = list(ram[0])
reward = 0.0
if not done:
if reward_type == 2:
old_goods = np.array(old_ram[4]["GOOD"])
old_bads = np.array(old_ram[4]["BAD"])
goods = np.array(ram[4]["GOOD"])
bads = np.array(ram[4]["BAD"])
mean_old_goods = np.mean(
old_goods) if len(old_goods) > 0 else 0.0
mean_old_bads = np.mean(old_bads) if len(old_bads) > 0 else 0.0
mean_goods = np.mean(goods) if len(goods) > 0 else 0.0
mean_bads = np.mean(bads) if len(bads) > 0 else 0.0
old_sum_dis = mean_old_goods - mean_old_bads
sum_dis = mean_goods - mean_bads
reward = old_sum_dis - gamma * sum_dis
if reward > 5.0:
reward = 5.0
elif reward < -5.0:
reward = -5.0
return reward
#############################################
#############################################
def _get_image(self):
image_rotated = np.fliplr(
np.rot90(self.game_state.getScreenRGB(),
3)) # Hack to fix the rotated image returned by ple
##########################################
# resize image
img = Image.fromarray(image_rotated)
img = img.resize((self.img_width, self.img_height), Image.ANTIALIAS)
image_resized = np.array(img).astype(np.uint8)
##########################################
return image_resized
@property
def _n_actions(self):
return len(self._action_set)
# return: (states, observations)
def _reset(self):
self.observation_space = spaces.Box(low=0,
high=255,
shape=(self.screen_width,
self.screen_height, 3),
dtype=np.uint8)
self.game_state.reset_game()
state = self._get_image()
return state
def _render(self, mode='human', close=False):
if close:
if self.viewer is not None:
self.viewer.close()
self.viewer = None
return
img = self._get_image()
if mode == 'rgb_array':
return img
elif mode == 'human':
from gym.envs.classic_control import rendering
if self.viewer is None:
self.viewer = rendering.SimpleImageViewer()
self.viewer.imshow(img)
def _seed(self, seed):
rng = np.random.RandomState(seed)
self.game_state.rng = rng
self.game_state.game.rng = self.game_state.rng
self.game_state.init()
if jump > not_jump:
return 0
else:
return 1
def update_Q(self, s, s_prime, reward, action):
self.Q[s[0], s[1], s[2], action] = (1 - self._alpha) * self.Q[
s[0], s[1], s[2], action] + self._alpha * (
reward + self._lambda *
np.max(self.Q[s_prime[0], s_prime[1], s_prime[2]]))
if __name__ == "__main__":
game = FlappyBird()
p = PLE(game, fps=30, display_screen=True)
agent = Agent(action_space=p.getActionSet(), grid_size=10)
p.init()
s = agent.get_current_state(game.getGameState())
episodes = 0
max_score = 0
while True:
# Find the optimal action based on the current state
max_action = agent.optimal_action(s)
current_score = p.score()
max_score = max(current_score, max_score)
# Perform the optimal action and return the reward
class MyEnv(Environment):
VALIDATION_MODE = 0
# original size is 288x512 so dividing
def __init__(self, rng, game=None, frame_skip=4,
ple_options={"display_screen": True, "force_fps":True, "fps":30}):
self._mode = -1
self._mode_score = 0.0
self._mode_episode_count = 0
self._frame_skip = frame_skip if frame_skip >= 1 else 1
self._random_state = rng
self._hist_size = 1
if game is None:
raise ValueError("Game must be provided")
self._ple = PLE(game, **ple_options)
self._ple.init()
self._actions = self._ple.getActionSet()
self._state_size = self._ple.getGameStateDims()[0]
self._state_saved = np.zeros((self._state_size), dtype=np.float32)
self.previous_score = 0.
self.episode_scores = []
def reset(self, mode):
if mode == MyEnv.VALIDATION_MODE:
if self._mode != MyEnv.VALIDATION_MODE:
self._mode = MyEnv.VALIDATION_MODE
self._mode_score = 0.0
self.episode_scores = []
self.previous_score = .0
self._mode_episode_count = 0
else:
self._mode_episode_count += 1
self.episode_scores.append(self._mode_score - self.previous_score)
self.previous_score = self._mode_score
elif self._mode != -1: # and thus mode == -1
self._mode = -1
# print("Dead at score {}".format(self._ple.game.getScore()))
self._ple.reset_game()
for _ in range(self._random_state.randint(self._hist_size)):
self._ple.act(self._ple.NOOP)
return [[[0] * self._state_size] * self._hist_size]
def act(self, action):
action = self._actions[action]
reward = 0
for _ in range(self._frame_skip):
reward += self._ple.act(action)
if self.inTerminalState():
break
self._state_saved = self._ple.getGameState()
self._mode_score += reward
if self.inTerminalState():
pass
return reward #np.sign(reward)
def summarizePerformance(self, test_data_set):
if self.inTerminalState() == False:
self._mode_episode_count += 1
maxscore = max(self.episode_scores) if len(self.episode_scores) else "N/A"
print("== Max score of episode is {} over {} episodes ==".format(
maxscore, self._mode_episode_count))
def inputDimensions(self):
return [(self._hist_size, self._state_size)]
def observationType(self, subject):
return np.float32
def nActions(self):
return len(self._actions)
def observe(self):
return [np.array(self._state_saved)]
def inTerminalState(self):
return self._ple.game_over()
def score(self, training=True, nb_episodes=10):
reward_values = {
"positive": 1.0,
"negative": 0.0,
"tick": 0.0,
"loss": 0.0,
"win": 0.0
}
env = PLE(FlappyBird(),
fps=30,
display_screen=False,
force_fps=True,
rng=None,
reward_values=reward_values)
env.init()
total_episodes = nb_episodes
score = 0
scores = []
while nb_episodes > 0:
# pick an action
state = env.game.getGameState()
action = self.policy(state)
# step the environment
reward = env.act(env.getActionSet()[action])
score += reward
# reset the environment if the game is over
if env.game_over() or score >= 100:
scores.append(score)
env.reset_game()
nb_episodes -= 1
score = 0
# print(nb_episodes)
avg_score = sum(scores) / float(len(scores))
confidence_interval = st.t.interval(0.95,
len(scores) - 1,
loc=np.mean(scores),
scale=st.sem(scores))
if np.isnan(confidence_interval[0]):
confidence_interval = (avg_score, avg_score)
print("Games played: {}".format(total_episodes))
print("Average score: {}".format(avg_score))
print("95 confidence interval: {}".format(confidence_interval))
if training:
score_file = "{}/scores.csv".format(self.name)
# If file doesn't exist, add the header
if not os.path.isfile(score_file):
with open(score_file, "ab") as f:
f.write(
"avg_score,episode_count,frame_count,interval_lower,interval_upper,min,max\n"
)
# Append scores to the file
with open(score_file, "ab") as f:
f.write("{},{},{},{},{},{},{}\n".format(
avg_score, self.episode_count, self.frame_count,
confidence_interval[0], confidence_interval[1],
min(scores), max(scores)))
count = 0
for score in scores:
if score >= 50:
count += 1
if count >= len(scores) * 0.9:
print("*** over 50 score in {} frames ***".format(
self.frame_count))
with open("pass_50.csv", "ab") as f:
f.write("{},{}\n".format(self.name, self.frame_count))
else:
with open("scores.txt", "ab") as f:
for score in scores:
f.write("{},{}\n".format(self.name, score))
class MyEnv(Environment):
VALIDATION_MODE = 0
memSize = 4
# original size is 288x512 so dividing
dividing_factor = 8
width = 288 // dividing_factor
height = 512 // dividing_factor
def __init__(self,
rng,
game=None,
frame_skip=4,
ple_options={
"display_screen": True,
"force_fps": True,
"fps": 30
}):
self._mode = -1
self._mode_score = 0.0
self._mode_episode_count = 0
self._frame_skip = frame_skip if frame_skip >= 1 else 1
self._random_state = rng
if game is None:
raise ValueError("Game must be provided")
self._ple = PLE(game, **ple_options)
self._ple.init()
w, h = self._ple.getScreenDims()
self._screen = np.empty((w, h), dtype=np.uint8)
self._reduced_screen = np.empty((self.width, self.height),
dtype=np.uint8)
self._actions = self._ple.getActionSet()
def reset(self, mode):
if mode == MyEnv.VALIDATION_MODE:
if self._mode != MyEnv.VALIDATION_MODE:
self._mode = MyEnv.VALIDATION_MODE
self._mode_score = 0.0
self._mode_episode_count = 0
else:
self._mode_episode_count += 1
elif self._mode != -1: # and thus mode == -1
self._mode = -1
print("Dead at score {}".format(self._ple.game.getScore()))
self._ple.reset_game()
# for _ in range(self._random_state.randint(15)):
# self._ple.act(self._ple.NOOP)
# self._screen = self._ple.getScreenGrayscale()
# cv2.resize(self._screen, (48, 48),
# self._reduced_screen,
# interpolation=cv2.INTER_NEAREST)
return [self.memSize * [self.width * [self.height * [0]]]]
def act(self, action):
action = self._actions[action]
reward = 0
for _ in range(self._frame_skip):
reward += self._ple.act(action)
if self.inTerminalState():
break
self._screen = self._ple.getScreenGrayscale()
self._reduced_screen = cv2.resize(self._screen,
(self.height, self.width),
interpolation=cv2.INTER_NEAREST)
cv2.imshow("debug", self._reduced_screen.T)
cv2.waitKey(1)
self._mode_score += reward
return np.sign(reward)
def summarizePerformance(self, test_data_set):
if self.inTerminalState() == False:
self._mode_episode_count += 1
mean = (self._mode_score / self._mode_episode_count
if self._mode_episode_count else "N/A")
print("== Mean score per episode is {} over {} episodes ==".format(
mean, self._mode_episode_count))
def inputDimensions(self):
return [(self.memSize, self.width, self.height)]
def observationType(self, subject):
return np.float32
def nActions(self):
return len(self._actions)
def observe(self):
return [np.array(self._reduced_screen) / 256.]
def inTerminalState(self):
return self._ple.game_over()
fps = 30 #fps we want to run at
frame_skip = 2
num_steps = 1
force_fps = False #slower speed
display_screen = True
reward = 0.0
max_noops = 20
nb_frames = 15000
#make a PLE instance.
p = PLE(game, fps=fps, frame_skip=frame_skip, num_steps=num_steps,
force_fps=force_fps, display_screen=display_screen)
#our Naive agent!
agent = NaiveAgent(p.getActionSet())
#init agent and game.
p.init()
#lets do a random number of NOOP's
for i in range(np.random.randint(0, max_noops)):
reward = p.act(p.NOOP)
#start our training loop
for f in range(nb_frames):
#if the game is over
if p.game_over():
p.reset_game()
obs = p.getScreenRGB()
EPSILON_DECAY = EPOCHS * STEPS_PER_EPOCHS
EPSILON_MIN = 0.01
EPSILON_DECAY_V = (EPSILON_MIN - EPSILON_START) / EPSILON_DECAY
SEED = 123456
epsilon = EPSILON_START
rng = np.random.RandomState(SEED)
game = flappy.FlappyClone()
env = PLE(game,
display_screen=True,
force_fps=True,
fps=30,
state_preprocessor=preprocessor,
rng=rng)
env.game.rewards["positive"] = 1
# env.game.rewards["tick"] = .01
qAgent = QAgent(env.getActionSet(), [s.size for s in scalers],
discount=.99,
learningRate=.2,
gridSize=GRID_SIZE,
epsilon=epsilon)
qAgent.jFilePath = os.path.join(folder, qAgent.jFilePath)
reward = 0.
clock = pygame.time.Clock()
laststate = None
lastticks = 0
periodJump = 0
action = None
nextTest = False
for e in range(EPOCHS):
avgloss = 0.
from ple.games.flappybird import FlappyBird
from ple import PLE
from humanagent import HumanAgent
game = FlappyBird()
p = PLE(game, fps=30, display_screen=True)
agent = HumanAgent(allowed_actions=p.getActionSet())
p.init()
reward = 0.0
nb_frames = 100
for i in range(nb_frames):
if p.game_over():
p.reset_game()
observation = p.getScreenRGB()
action = agent.pickAction(reward, observation)
reward = p.act(action)
def launch(args, defaults, description):
"""
Execute a complete training run.
"""
logging.basicConfig(level=logging.INFO)
parameters = process_args(args, defaults, description)
rewards = {}
try:
module = importlib.import_module("ple.games.%s" % parameters.game.lower())
game = getattr(module, parameters.game)
if parameters.game == "FlappyBird":
game = game()
elif parameters.game == "WaterWorld":
game = game(width=84, height=84, num_creeps=6)
else:
game = game(width=84, height=84)
except:
raise ValueError("The game %s could not be found. Try using the classname, it is case sensitive." % parameters.game)
if parameters.deterministic:
rng = np.random.RandomState(123456)
else:
rng = np.random.RandomState()
if parameters.cudnn_deterministic:
theano.config.dnn.conv.algo_bwd = 'deterministic'
env = PLE(
game,
fps=60,
force_fps=parameters.force_fps,
display_screen=parameters.display_screen,
reward_values=rewards,
rng=rng
)
num_actions = len(env.getActionSet())
if parameters.nn_file is None:
network = q_network.DeepQLearner(defaults.RESIZED_WIDTH,
defaults.RESIZED_HEIGHT,
num_actions,
parameters.phi_length,
parameters.discount,
parameters.learning_rate,
parameters.rms_decay,
parameters.rms_epsilon,
parameters.momentum,
parameters.clip_delta,
parameters.freeze_interval,
parameters.batch_size,
parameters.network_type,
parameters.update_rule,
parameters.batch_accumulator,
rng)
else:
handle = open(parameters.nn_file, 'r')
network = cPickle.load(handle)
agent = ple_agent.NeuralAgent(network,
parameters.epsilon_start,
parameters.epsilon_min,
parameters.epsilon_decay,
parameters.replay_memory_size,
parameters.experiment_prefix,
parameters.replay_start_size,
parameters.update_frequency,
rng)
experiment = ple_experiment.PLEExperiment(env, agent,
defaults.RESIZED_WIDTH,
defaults.RESIZED_HEIGHT,
parameters.resize_method,
parameters.epochs,
parameters.steps_per_epoch,
parameters.steps_per_test,
parameters.frame_skip,
parameters.death_ends_episode,
parameters.max_start_nullops,
rng)
env.init()
experiment.run()
import numpy as np
from ple import PLE
from ple.games.waterworld import WaterWorld
# lets adjust the rewards our agent recieves
rewards = {
"tick": -0.01, # each time the game steps forward in time the agent gets -0.1
"positive": 1.0, # each time the agent collects a green circle
"negative": -5.0, # each time the agent bumps into a red circle
}
# make a PLE instance.
# use lower fps so we can see whats happening a little easier
game = WaterWorld(width=256, height=256, num_creeps=8)
p = PLE(game, fps=15, force_fps=False, display_screen=True,
reward_values=rewards)
# we pass in the rewards and PLE will adjust the game for us
p.init()
actions = p.getActionSet()
for i in range(1000):
if p.game_over():
p.reset_game()
action = actions[np.random.randint(0, len(actions))] # random actions
reward = p.act(action)
print "Score: {:0.3f} | Reward: {:0.3f} ".format(p.score(), reward)
class Bot():
"""
This is our Test agent. It's gonna pick some actions after training!
"""
def __init__(self, lr):
self.lr = lr
self.game = Pixelcopter(width=480, height=480)
self.p = PLE(self.game, fps=60, display_screen=True)
self.actions = self.p.getActionSet()
#def pickAction(self, reward, obs):
# return random.choice(self.actions)
def frame_step(act_inp):
terminal = False
reward = self.p.act(act_inp)
if self.p.game_over():
self.p.reset_game()
terminal = True
reward = -1
else:
reward = 1
self.score = self.p.getScore()
img = self.p.getScreenGrayscale()
img = transform.resize(img, (80, 80))
img = np.ravel(exposure.rescale_intensity(img, out_range=(0, 255)))
return img, reward, terminal
def build_model(self):
print("Building the model..")
model = Sequential()
model.add(
Convolution2D(32,
8,
8,
subsample=(4, 4),
border_mode='same',
input_shape=(img_rows, img_cols,
img_channels))) #80*80*4
model.add(Activation('relu'))
model.add(Convolution2D(64, 4, 4, subsample=(2, 2),
border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, subsample=(1, 1),
border_mode='same'))
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dense(2))
adam = Adam(lr=self.lr)
model.compile(loss='mse', optimizer=adam)
self.model = model
print("Finished building the model..")
def trainNetwork(self, mode):
D = deque()
x_t, r_0, terminal = self.frame_step(self.actions[0])
x_t = x_t / 255.0
s_t = np.stack((x_t, x_t, x_t, x_t), axis=2)
#print (s_t.shape)
#need to reshape for keras
s_t = s_t.reshape(1, s_t.shape[0], s_t.shape[1],
s_t.shape[2]) #1*80*80*4
if mode == 'Run':
OBSERVE = 999999999 #We keep observe, never train
epsilon = FINAL_EPSILON
print("Now we load weight")
self.model.load_weights("model.h5")
adam = Adam(lr=self.lr)
self.model.compile(loss='mse', optimizer=adam)
print("Weight load successfully")
else: #We go to training mode
OBSERVE = OBSERVATION
epsilon = INITIAL_EPSILON
import numpy as np
from keras.models import load_model
from challenge_utils import process_screen
from collections import deque
from ple.games.flappybird import FlappyBird
from ple import PLE
deepQnet = load_model('model.h5')
game = FlappyBird(graphics="fixed")
p = PLE(game, fps=30, frame_skip=1, num_steps=1)
list_actions = p.getActionSet()
size_img = (80, 80)
frameDeque = deque([
np.zeros(size_img),
np.zeros(size_img),
np.zeros(size_img),
np.zeros(size_img)
],
maxlen=4)
def FlappyPolicy(state, screen):
global deepQnet
global frameDeque
global list_actions
x = process_screen(screen)
# Reinitialize the deque if we start a new game
if not np.any(x[10:, :]): # if everything in front of Flappy is black
frameDeque = deque([
class NaiveAgent():
"""
This is our naive agent. It picks actions at random!
"""
def __init__(self, actions):
self.actions = actions
def pickAction(self, reward, obs):
return self.actions[np.random.randint(0, len(self.actions))]
###################################
game = Doom(scenario="take_cover")
env = PLE(game)
agent = NaiveAgent(env.getActionSet())
env.init()
reward = 0.0
for f in range(15000):
#if the game is over
if env.game_over():
env.reset_game()
action = agent.pickAction(reward, env.getScreenRGB())
reward = env.act(action)
if f > 2000:
env.display_screen = True
env.force_fps = False
def train(self):
"""Train."""
logs_path = self.args.logs_path
video_path = self.args.video_path
restore = self.args.restore
train = self.args.train
# Initial PLE environment
os.putenv('SDL_VIDEODRIVER', 'fbcon')
os.environ["SDL_VIDEODRIVER"] = "dummy"
# Design reward
reward_values = {
"positive": 1,
"tick": 0.1,
"loss": -1,
}
# Create FlappyBird game env
env = PLE(FlappyBird(),
display_screen=False,
reward_values=reward_values)
# Gets the actions FlappyBird supports
action_set = env.getActionSet()
replay_buffer = ReplayBuffer(self.hparams.replay_buffer_size)
agent = Agent(action_set, self.hparams)
# restore model
if restore:
agent.restore(restore)
reward_logs = []
loss_logs = []
for episode in range(1, self.hparams.total_episode + 1):
# reset env
env.reset_game()
env.act(0)
obs = convert(env.getScreenGrayscale())
state = np.stack([[obs for _ in range(4)]], axis=0)
t_alive = 0
total_reward = 0
if episode % self.hparams.save_video_frequency == 0 and episode > self.hparams.initial_observe_episode:
agent.stop_epsilon()
frames = [env.getScreenRGB()]
while not env.game_over():
action = agent.take_action(state)
reward = env.act(action_set[action])
if episode % self.hparams.save_video_frequency == 0 and episode > self.hparams.initial_observe_episode:
frames.append(env.getScreenRGB())
obs = convert(env.getScreenGrayscale())
obs = np.reshape(obs, [1, 1, obs.shape[0], obs.shape[1]])
state_new = np.append(state[:, 1:, ...], obs, axis=1)
action_onehot = np.zeros(len(action_set))
action_onehot[action] = 1
t_alive += 1
total_reward += reward
replay_buffer.append(
(state, action_onehot, reward, state_new, env.game_over()))
state = state_new
# save video
if episode % self.hparams.save_video_frequency == 0 and episode > self.hparams.initial_observe_episode:
os.makedirs(video_path, exist_ok=True)
clip = make_video(frames, fps=60).rotate(-90)
clip.write_videofile(os.path.join(
video_path, 'env_{}.mp4'.format(episode)),
fps=60)
agent.restore_epsilon()
print('Episode: {} t: {} Reward: {:.3f}'.format(
episode, t_alive, total_reward))
# danger
mp4list = glob.glob('./video_XXX/*.mp4')
if len(mp4list) > 0:
latest = mp4list[0]
latest_timestamp = os.path.getmtime(mp4list[0])
for mp4 in mp4list:
ts = os.path.getmtime(mp4)
if (ts > latest_timestamp):
latest_timestamp = ts
latest = mp4
video = io.open(latest, 'r+b').read()
encoded = base64.b64encode(video)
ipythondisplay.display(
HTML(data='''<video alt="test" autoplay
loop controls style="height: 400px;">
<source src="data:video/mp4;base64,{0}" type="video/mp4" />
</video>'''.format(encoded.decode('ascii'))))
#end danger
else:
print("Could not find video")
if episode > self.hparams.initial_observe_episode and train:
# save model
if episode % self.hparams.save_logs_frequency == 0:
agent.save(episode, logs_path)
np.save(os.path.join(logs_path, 'loss.npy'),
np.array(loss_logs))
np.save(os.path.join(logs_path, 'reward.npy'),
np.array(reward_logs))
# update target network
if episode % self.hparams.update_target_frequency == 0:
agent.update_target_network()
# sample batch from replay buffer
batch_state, batch_action, batch_reward, batch_state_new, batch_over = replay_buffer.sample(
self.hparams.batch_size)
# update policy network
loss = agent.update_Q_network(batch_state, batch_action,
batch_reward, batch_state_new,
batch_over)
loss_logs.extend([[episode, loss]])
reward_logs.extend([[episode, total_reward]])
# print reward and loss
if episode % self.hparams.show_loss_frequency == 0:
print(
'Episode: {} t: {} Reward: {:.3f} Loss: {:.3f}'.format(
episode, t_alive, total_reward, loss))
agent.update_epsilon()
class MyEnv(Environment):
VALIDATION_MODE = 0
def __init__(self, rng, game=None, frame_skip=4,
ple_options={"display_screen": True, "force_fps":True, "fps":30}):
self._mode = -1
self._mode_score = 0.0
self._mode_episode_count = 0
self._frameSkip = frame_skip if frame_skip >= 1 else 1
self._random_state = rng
if game is None:
raise ValueError("Game must be provided")
self._ple = PLE(game, **ple_options)
self._ple.init()
w, h = self._ple.getScreenDims()
self._screen = np.empty((h, w), dtype=np.uint8)
self._reducedScreen = np.empty((48, 48), dtype=np.uint8)
self._actions = self._ple.getActionSet()
def reset(self, mode):
if mode == MyEnv.VALIDATION_MODE:
if self._mode != MyEnv.VALIDATION_MODE:
self._mode = MyEnv.VALIDATION_MODE
self._mode_score = 0.0
self._mode_episode_count = 0
else:
self._mode_episode_count += 1
elif self._mode != -1: # and thus mode == -1
self._mode = -1
self._ple.reset_game()
for _ in range(self._random_state.randint(15)):
self._ple.act(self._ple.NOOP)
self._screen = self._ple.getScreenGrayscale()
cv2.resize(self._screen, (48, 48), self._reducedScreen, interpolation=cv2.INTER_NEAREST)
return [4 * [48 * [48 * [0]]]]
def act(self, action):
action = self._actions[action]
reward = 0
for _ in range(self._frameSkip):
reward += self._ple.act(action)
if self.inTerminalState():
break
self._screen = self._ple.getScreenGrayscale()
cv2.resize(self._screen, (48, 48), self._reducedScreen, interpolation=cv2.INTER_NEAREST)
self._mode_score += reward
return np.sign(reward)
def summarizePerformance(self, test_data_set):
if self.inTerminalState() == False:
self._mode_episode_count += 1
print("== Mean score per episode is {} over {} episodes ==".format(self._mode_score / self._mode_episode_count, self._mode_episode_count))
def inputDimensions(self):
return [(4, 48, 48)]
def observationType(self, subject):
return np.uint8
def nActions(self):
return len(self._actions)
def observe(self):
return [np.array(self._reducedScreen)]
def inTerminalState(self):
return self._ple.game_over()
#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)