def test():
pygame.init()
agent = DQNAgent(output_dim=3)
agent.model.load_weights('weights.hdf5')
# while counter_games < 150:
# Initialize classes
game = Game(440, 440)
player1 = game.player
field0 = game.field
# Perform first move
record = 0
initialize_game(player1, game, field0, agent)
if display_option:
display(player1, field0, game, record)
while not game.crash:
# get old state
state_old = agent.get_state(game, player1, field0)
# predict action based on the old state
prediction = agent.model.predict(state_old)
final_move = np.argmax(prediction[0])
print("move {} with prediction : {}".format(final_move, prediction))
# perform new move and get new state
player1.do_move(final_move, field0, game)
record = get_record(game.score, record)
if display_option:
pygame.time.wait(speed)
display(player1, field0, game, record)
def train(display_on, speed, params):
pygame.init()
pygame.font.init()
agent = DQNAgent(params)
counter_games = 0
high_score = 0
score_plot = []
counter_plot = []
while counter_games < params['episodes']:
game = Game(440, 440, high_score)
if display_on:
game.update_display()
while not game.crash:
if handle_game_event(game):
return
# agent.epsilon is set to give randomness to actions
agent.epsilon = 1 - (counter_games *
params['epsilon_decay_linear'])
state = game.get_state()
move = agent.get_move(state)
game.do_move(move)
new_state = game.get_state()
reward = get_reward(game)
# train short memory base on the new action and state
agent.train_short_memory(state, move, reward, new_state,
game.crash)
agent.remember(state, move, reward, new_state, game.crash)
if display_on:
game.update_display()
pygame.time.wait(speed)
counter_games += 1
print(f'Game {counter_games} Score: {game.score}')
high_score = game.high_score
score_plot.append(game.score)
counter_plot.append(counter_games)
agent.replay_memory(params['batch_size'])
agent.model.save_weights(params['weights_path'])
pygame.quit()
plot_seaborn(counter_plot, score_plot)
def script():
agents = [BetterRandomAgent(), BetterPathAgent(), DQNAgent()]
field_sizes = [8, 20]
n_games = 1000
for size in field_sizes:
for agent in agents:
run_n_logs(agent, n_games, size)
def __init__(self, env_name='harvest', num_agents=1):
self.env_name = env_name
if env_name == 'harvest':
print('Initializing Harvest environment')
self.env = HarvestEnv(ascii_map=HARVEST_MAP_CPR,
num_agents=num_agents,
render=True)
elif env_name == 'cleanup':
print('Initializing Cleanup environment')
self.env = CleanupEnv(num_agents=num_agents, render=True)
else:
print('Error! Not a valid environment type')
return
self.num_agents = num_agents
self.agent_policies = []
self.agents = list(self.env.agents.values())
# print(agents[0].action_space)
self.action_dim = self.agents[0].action_space.n
for _ in range(num_agents):
# TODO right now only using 1 frame, update later to look back x (e.g. 4) frames. Later RNN/LSTM
neural_net = ConvFC(
conv_in_channels=
3, # harvest specific input is 15x15x3 (HARVEST_VIEW_SIZE = 7)
conv_out_channels=3,
input_size=15,
hidden_size=64,
output_size=self.action_dim)
self.agent_policies.append(
DQNAgent(0, self.action_dim - 1, neural_net))
self.env.reset()
def __init__(self):
self.amountOfSimulations = 0
self.maxSpeed = 4
self.score = 0
self.previousScore = 0
self.highscore = 0
self.highscoreTime = 0
#self.delayCounter = 0
#self.delay = randint(5,10)
self.keepRunning = True
self.agent = DQNAgent()
self.mysystem = chrono.ChSystemNSC()
self.ground = theBattleground.theBattleground(self.mysystem)
self.createRobot(self.mysystem)
self.createApplication()
self.run()
def train(epoch=10):
pygame.init()
agent = DQNAgent(output_dim=5)
counter_games = 0
score_plot = []
counter_plot = []
record = 0
while counter_games < epoch:
# Initialize classes
game = Game(440, 440)
player1 = game.player1
player2 = game.player2
field0 = game.field
# Perform first move
initialize_game(player1, player2, game, field0, agent)
if display_option:
display(player1, player2, field0, game, record)
game_epoch = 0
while not game.crash:
#agent.epsilon is set to give randomness to actions
agent.epsilon = 50 - game_epoch
train_each_epoch(agent, game, field0, player1, [player2], game_epoch)
train_each_epoch(agent, game, field0, player2, [player1], game_epoch)
record = get_record(game.player1.score, game.player2.score, record)
if display_option:
display(player1, player2, field0, game, record)
pygame.time.wait(speed)
game_epoch += 1
game.crash = not (game.player1.display or game.player2.display)
agent.replay_new(agent.memory)
counter_games += 1
print('Game', counter_games, ' Score:', game.player1.score, game.player2.score)
score_plot.append(game.player1.score)
counter_plot.append(counter_games)
print(counter_plot)
agent.model.save_weights('weights_multi.hdf5')
plot_seaborn(counter_plot, score_plot)
def test():
global dqn_save, checkFirst, dqn_agent
if checkFirst:
dqn_agent = DQNAgent(num_state, num_action, num_hidden_node)
checkFirst = False
else:
dqn_agent = dqn_save
dqn_save = dqn_agent
return dqn_agent
def __init__(self,
env_name,
state_size,
frame_size,
agent="DQN",
render=True,
train=True,
load_path="checkpoint.pth"):
self.env_name = env_name
self.frame_size = frame_size
self.render = render
self.train = train
self.env = gym.make(self.env_name)
if (agent == "DQN"):
self.agent = DQNAgent(state_size, frame_size,
self.env.action_space.n, load_path)
else:
raise Exception("Agent not Found")
self.initialized = False
self.epoch = 0
def play(display_on, speed, params):
pygame.init()
pygame.font.init()
agent = DQNAgent(params)
agent.epsilon = 0
counter_games = 0
high_score = 0
score_plot = []
counter_plot = []
while counter_games < params['episodes']:
game = Game(440, 440, high_score)
if display_on:
game.update_display()
while not game.crash:
if handle_game_event(game):
return
state = game.get_state()
move = agent.get_move(state)
game.do_move(move)
if display_on:
game.update_display()
pygame.time.wait(speed)
counter_games += 1
print(f'Game {counter_games} Score: {game.score}')
high_score = game.high_score
score_plot.append(game.score)
counter_plot.append(counter_games)
pygame.quit()
plot_seaborn(counter_plot, score_plot)
def __init__(self):
self.rows = 0
self.cols = 0
self.path_for_my_units = None
self.units_points=np.zeros(shape=(9,),dtype=int)
self.epsilon=0
self.agent=DQNAgent()
self.picker=Picker()
self.state_old=np.zeros(121)
self.new_state=[]
self.picked=np.zeros(9,dtype=int)
self.prediction=[[]]
class QLearningBehaviour(Behaviour):
def __init__(self,visualRange=3):
self.agent = DQNAgent(visualRange)
self.age = 0
self.current_move = None
self.current_input = None
#def on_init(self,visualRange):
def decide(self, input):
self.current_input = input
final_move = None
epsilon = 150 - self.age
if epsilon < 15:
epsilon = 15
if randint(0, 100) > epsilon:
final_move = randint(0,5)
else:
#get old state
#state_old = agent.get_state(game, player1, food1)
state_old = np.asarray(input)
prediction = self.agent.model.predict(input.reshape((1,-1)))
#final_move = to_categorical(np.argmax(prediction[0]), num_classes=5)
final_move = np.argmax(prediction[0])
self.current_move = final_move
self.age += 1
print(final_move)
return final_move
#perform new move and get new state
#self.do_move(final_move, self.x,self.y,agent)
#state_new = agent.get_state(game, player1, food1)
def feedback(self, reward, state):
#set treward for the new state
#reward = agent.set_reward(input, move,reward)
state_new = np.asarray(state)
#train short memory base on the new action and state
state_old = self.current_input #
final_move = to_categorical(self.current_move, num_classes=6)
self.agent.train_short_memory(state_old, final_move, reward, state_new)
# store the new data into a long term memory
self.agent.remember(state_old, final_move, reward, state_new)
#record = get_record(game.score, record)
#if display_option:
# display(player1, food1, game, record)
# pygame.time.wait(speed)
self.agent.replay_new(self.agent.memory) #???
def train():
env = gym.make('CartPole-v0')
agent = DQNAgent(env=env)
num_episodes = 200
for i_episode in range(num_episodes):
state = env.reset()
total_reward = 0
while True:
action = agent.get_action(state)
next_state, reward, done, info = env.step(action)
total_reward += reward
update_array = [state, action, reward, next_state, done]
agent.update(update_array)
state = next_state
if done:
print("Episode ", i_episode, ": ", total_reward, " epsilon: ", agent.epsilon)
break
agent.save('myClassModel')
env.close()
def play(display_on, speed, params):
pygame.init()
pygame.font.init()
agent = DQNAgent(params)
counter_games = 0
high_score = 0;
#score_plot = []
#counter_plot = []
while counter_games < params['episodes']:
game = Game(440, 440, high_score)
if display_on:
game.update_display()
while not game.crash:
if handle_game_event(game):
return
state = game.get_state()
prediction = agent.model.predict(state.reshape((1,11)))
move = to_categorical(np.argmax(prediction[0]), num_classes=3)
game.do_move(move)
if display_on:
game.update_display()
pygame.time.wait(speed)
counter_games += 1
print(f'Game {counter_games} Score: {game.score}')
high_score = game.high_score
pygame.quit()
def train(epoch=10):
pygame.init()
agent = DQNAgent(output_dim=3)
counter_games = 0
score_plot = []
counter_plot = []
record = 0
while counter_games < epoch:
# Initialize classes
game = Game(440, 440)
player1 = game.player
field0 = game.field
# Perform first move
initialize_game(player1, game, field0, agent)
if display_option:
display(player1, field0, game, record)
game_epoch = 0
while not game.crash:
#agent.epsilon is set to give randomness to actions
agent.epsilon = 50 - game_epoch
#get old state
state_old = agent.get_state(game, player1, field0)
#perform random actions based on agent.epsilon, or choose the action
if randint(0, 100) < agent.epsilon:
final_move = randint(0, 2)
# print("random with prob {}".format(agent.epsilon))
else:
# predict action based on the old state
prediction = agent.model.predict(state_old)
final_move = np.argmax(prediction[0])
print("prediction : {}".format(prediction))
# print("move: {} to position ({}, {})".format(final_move, player1.x, player1.y))
#perform new move and get new state
player1.do_move(final_move, field0, game)
if game_epoch >= 19:
# get new state
state_new = agent.get_state(game, player1, field0)
#set treward for the new state
reward = agent.set_reward(player1, game.crash, final_move)
#train short memory base on the new action and state
agent.train_short_memory(state_old, final_move, reward,
state_new, game.crash)
# store the new data into a long term memory
if_remember = False
if game.crash:
agent.remember(state_old, final_move, reward, state_new,
game.crash)
if_remember = True
# print("remember this move with reward {}".format(reward))
elif final_move == 0 and randint(1, 20) < 20:
agent.remember(state_old, final_move, reward, state_new,
game.crash)
if_remember = True
# print("remember this move with reward {}".format(reward))
elif final_move != 0 and randint(1, 20) < 20:
agent.remember(state_old, final_move, reward, state_new,
game.crash)
if_remember = True
# print("remember this move with reward {}".format(reward))
print(
"actual move {} to ({}, {}) gets reward {} - remember {}".
format(final_move, player1.x, player1.y, reward,
if_remember))
# explore other move
if final_move == 0: # no
# 1 left
explore_moves(game, field0, agent, player1, state_old, 1,
max(0, player1.x - 1), player1.y)
# 2 right
explore_moves(game, field0, agent, player1, state_old, 2,
min(player1.x + 1, 21), player1.y)
elif final_move == 1: # left
# 0 no
explore_moves(game, field0, agent, player1, state_old, 0,
min(player1.x + 1, 21), player1.y)
# 2 right
explore_moves(game, field0, agent, player1, state_old, 2,
min(player1.x + 2, 21), player1.y)
elif final_move == 2: # right
# 0 no
explore_moves(game, field0, agent, player1, state_old, 0,
max(0, player1.x - 1), player1.y)
# 2 right
explore_moves(game, field0, agent, player1, state_old, 1,
max(0, player1.x - 2), player1.y)
record = get_record(game.score, record)
if display_option:
display(player1, field0, game, record)
pygame.time.wait(speed)
game_epoch += 1
agent.replay_new(agent.memory)
counter_games += 1
print('Game', counter_games, ' Score:', game.score)
score_plot.append(game.score)
counter_plot.append(counter_games)
if game.score >= record:
agent.model.save_weights(modelFile + '/weights.hdf5')
agent.model.save_weights(modelFile + '/weightsFinal.hdf5')
plot_seaborn(counter_plot, score_plot)
def run(params):
"""
Run the DQN algorithm, based on the parameters previously set.
"""
pygame.init()
agent = DQNAgent(params)
agent = agent.to(DEVICE)
agent.optimizer = optim.Adam(agent.parameters(),
weight_decay=0,
lr=params['learning_rate'])
counter_games = 0
score_plot = []
counter_plot = []
record = 0
total_score = 0
while counter_games < params['episodes']:
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
quit()
# Initialize classes
game = Game(440, 440)
player1 = game.player
food1 = game.food
# Perform first move
initialize_game(player1, game, food1, agent, params['batch_size'])
if params['display']:
display(player1, food1, game, record)
while not game.crash:
if not params['train']:
agent.epsilon = 0.01
else:
# agent.epsilon is set to give randomness to actions
agent.epsilon = 1 - (counter_games *
params['epsilon_decay_linear'])
# get old state
state_old = agent.get_state(game, player1, food1)
# perform random actions based on agent.epsilon, or choose the action
if random.uniform(0, 1) < agent.epsilon:
final_move = np.eye(3)[randint(0, 2)]
else:
# predict action based on the old state
with torch.no_grad():
state_old_tensor = torch.tensor(
state_old.reshape(
(1, 11)), dtype=torch.float32).to(DEVICE)
prediction = agent(state_old_tensor)
final_move = np.eye(3)[np.argmax(
prediction.detach().cpu().numpy()[0])]
# perform new move and get new state
player1.do_move(final_move, player1.x, player1.y, game, food1,
agent)
state_new = agent.get_state(game, player1, food1)
# set reward for the new state
reward = agent.set_reward(player1, game.crash)
if params['train']:
# train short memory base on the new action and state
agent.train_short_memory(state_old, final_move, reward,
state_new, game.crash)
# store the new data into a long term memory
agent.remember(state_old, final_move, reward, state_new,
game.crash)
record = get_record(game.score, record)
if params['display']:
display(player1, food1, game, record)
pygame.time.wait(params['speed'])
if params['train']:
agent.replay_new(agent.memory, params['batch_size'])
counter_games += 1
total_score += game.score
print(f'Game {counter_games} Score: {game.score}')
score_plot.append(game.score)
counter_plot.append(counter_games)
mean, stdev = get_mean_stdev(score_plot)
if params['train']:
model_weights = agent.state_dict()
torch.save(model_weights, params["weights_path"])
if params['plot_score']:
plot_seaborn(counter_plot, score_plot, params['train'])
return total_score, mean, stdev
import numpy as np
import gym
from DQN import DQNAgent
from utils import plot_learning
env = gym.make('LunarLander-v2')
lr = 0.001
n_games = 500
agent = DQNAgent(gamma=0.99,
epsilon=1.0,
lr=lr,
input_dims=env.observation_space.shape,
n_actions=env.action_space.n,
mem_size=1000000,
batch_size=64,
epsilon_end=0.01)
scores = []
eps_history = []
for i in range(n_games):
done = False
score = 0
observation = env.reset()
while not done:
# env.render()
action = agent.choose_action(observation)
observation_, reward, done, _ = env.step(action)
score += reward
agent.store_transition(observation, action, reward, observation_, done)
observation = observation_
con_3 = Convolution1D(32, 3)(con_2)
con_4 = Convolution1D(32, 3)(con_3)
flt_1 = Flatten()(con_4)
den_1 = Dense(32, activation='sigmoid')(flt_1)
den_2 = Dense(32, activation='sigmoid')(den_1)
den_3 = Dense(16, activation='sigmoid')(den_2)
den_4 = Dense(16, activation='sigmoid')(den_3)
predictions = Dense(8, activation="linear")(den_4)
model = Model(inputs=inputs, outputs=predictions)
model.compile(loss='mean_squared_error', optimizer='sgd')
input_test = list()
for i in range(0, 10):
input_test.append([i for i in range(0, 85)])
print(model.predict(np.array(input_test).reshape(1, 85, -1)))
trainer = Trainer(model)
pool = ThreadPoolExecutor(max_workers=255)
for i in range(0, 255):
pool.submit(
DQNAgent(20, reward, model=trainer).run_bot_join)
DQNAgent(20, reward, model=trainer).run_bot_join()
pool.shutdown(wait=True)
print("Shut down!")
model.save('trained_model.h5')
def run(display_option, speed, params):
pygame.init()
agent = DQNAgent(params)
weights_filepath = params['weights_path']
if params['load_weights']:
agent.model.load_weights(weights_filepath)
print("weights loaded")
counter_games = 0
score_plot = []
counter_plot = []
record = 0
while counter_games < params['episodes']:
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
quit()
# Initialize classes
game = Game(440, 440)
player1 = game.player
food1 = game.food
# Perform first move
initialize_game(player1, game, food1, agent, params['batch_size'])
if display_option:
display(player1, food1, game, record)
while not game.crash:
if not params['train']:
agent.epsilon = 0
else:
# agent.epsilon is set to give randomness to actions
agent.epsilon = 1 - (counter_games *
params['epsilon_decay_linear'])
# get old state
state_old = agent.get_state(game, player1, food1)
# perform random actions based on agent.epsilon, or choose the action
if randint(0, 1) < agent.epsilon:
final_move = to_categorical(randint(0, 2), num_classes=3)
else:
# predict action based on the old state
prediction = agent.model.predict(state_old.reshape((1, 11)))
final_move = to_categorical(np.argmax(prediction[0]),
num_classes=3)
# perform new move and get new state
player1.do_move(final_move, player1.x, player1.y, game, food1,
agent)
state_new = agent.get_state(game, player1, food1)
# set reward for the new state
reward = agent.set_reward(player1, game.crash)
if params['train']:
# train short memory base on the new action and state
agent.train_short_memory(state_old, final_move, reward,
state_new, game.crash)
# store the new data into a long term memory
agent.remember(state_old, final_move, reward, state_new,
game.crash)
record = get_record(game.score, record)
if display_option:
display(player1, food1, game, record)
pygame.time.wait(speed)
if params['train']:
agent.replay_new(agent.memory, params['batch_size'])
counter_games += 1
print(f'Game {counter_games} Score: {game.score}')
score_plot.append(game.score)
counter_plot.append(counter_games)
if params['train']:
agent.model.save_weights(params['weights_path'])
plot_seaborn(counter_plot, score_plot)
def run():
agent = DQNAgent(size)
counter_games = 0
score_plot = []
counter_plot = []
record = 0
while counter_games < games:
# Initialize classes
game = Game(size, size)
player1 = game.player
food1 = game.food
# Perform first move
initialize_game(player1, game, food1, agent)
if display_option:
display(player1, food1, game, record)
while not game.crash:
#agent.epsilon is set to give randomness to actions
agent.epsilon = (games * 0.4) - counter_games
#get old state
state_old = agent.get_state(game, player1, food1)
#perform random actions based on agent.epsilon, or choose the action
if randint(0, games) < agent.epsilon:
final_move = to_categorical(randint(0, 2), num_classes=3)
else:
# predict action based on the old state
prediction = agent.model.predict(state_old.reshape((1, agent.size)))
final_move = to_categorical(np.argmax(prediction[0]), num_classes=3)
#perform new move and get new state
player1.do_move(final_move, player1.x, player1.y, game, food1, agent)
state_new = agent.get_state(game, player1, food1)
#set reward for the new state
reward = agent.set_reward(player1, game.crash)
#train short memory base on the new action and state
agent.train_short_memory(state_old, final_move, reward, state_new, game.crash)
# store the new data into a long term memory
agent.remember(state_old, final_move, reward, state_new, game.crash)
record = get_record(game.score, record)
if display_option:
display(player1, food1, game, record)
pygame.time.wait(speed)
agent.replay_new(agent.memory)
counter_games += 1
score_plot.append(game.score)
counter_plot.append(counter_games)
print('Game', counter_games, ' Score:', game.score, 'Last 10 Avg:', np.mean(score_plot[-10:]))
agent.model.save_weights('weights.hdf5')
plot_seaborn(counter_plot, score_plot)
# #################################################
# Start timer
start = timeit.default_timer()
if DDQN is True:
# Name of weights + Initialize DDQN class + run training + evaluate
name_of_weights_DDQN = 'weights_DDQN.h5'
name_of_plot = 'train_plot_DDQN'
pre_agent = DDQNAgent()
aft_agent = run(pre_agent, name_of_weights_DDQN, name_of_plot)
evaluate_network(aft_agent, name_of_weights_DDQN)
else:
# Name of weights + Initialize DQN target class + run training + evaluatn
if target:
name_of_weights_DQN = 'weights_DQN_target.h5'
name_of_plot = 'train_plot_DQN_target'
else:
name_of_weights_DQN = 'weights_DQN_not_target_2.h5'
name_of_plot = 'train_plot_DQN_not_target_2'
pre_agent = DQNAgent(target)
aft_agent = run(pre_agent, name_of_weights_DQN, name_of_plot)
evaluate_network(aft_agent, name_of_weights_DQN)
stop = timeit.default_timer()
print('Total run time: %i min' % ((stop - start)/60))
diff = target_q_values - q_values
self.memory.update_priorities(indices, diff.detach().sequeeze().abs().cpu().numpy().tolist())
return loss
if __name__ == "__main__":
log_dir = "./dqn-p/1"
if not osp.exists(log_dir):
os.makedirs(log_dir)
env_id = "Pong-v0"
env = gym.make(env_id)
# env = gym.wrappers.Monitor(env, osp.join(log_dir,"record"))
env = WrapPytorch(env)
agent = DQNAgent(env, log_dir=log_dir)
# agent.load(log_dir)
episode_rewards = []
ep = 0
obs = env.reset()
episode_reward = 0
for frame in range(Config.MAX_FRAMES):
# print("frame", frame)
# env.render()
epsilon = Config.epsilon_by_frame(frame)
action = agent.get_action(obs, epsilon)
prev_obs = obs
obs, reward, done, _ = env.step(action)
episode_reward += reward
agent.update(prev_obs, action, reward, obs, frame)
attacks["spsa"] = {
"epsilon": 0.05,
"delta": 0.005,
"num_steps": 5,
"spsa_iters": 5,
"spsa_samples": 2,
"y_target": 0,
"is_targeted": True
} #, "early_stop_loss_threshold": -1.}
tf.reset_default_graph()
sess = tf.Session()
env = gym.make('PongNoFrameskip-v4')
statsFolder = "../experiments/pong/"
dqn = DQNAgent(env, sess, "../ckpts/dqn/pong_final/dqn_final.ckpt")
attackModel = AttackModel(dqn)
stats = testAttacks(dqn, attackModel, attacks, gamesNum, attackProbs,
actionProbThr, attDetThr, statsFolder)
#adv training test
tf.reset_default_graph()
sess = tf.Session()
env = gym.make('PongNoFrameskip-v4')
statsFolder = "../experiments/pong_adv_training_0.015/"
dqn = DQNAgent(env, sess,
"../ckpts/dqn/pong_adv_training/0.015/dqn_final.ckpt")
attackModel = AttackModel(dqn)
def run_game():
env = Tetris()
episodes = 2000
max_steps = None
discount = 0.98
replay_mem_size = 20000
minibatch_size = 512
epsilon = 1
epsilon_min = 0
epsilon_stop_episode = 1500
learning_rate = 5e-3
epochs = 1
show_every = 50
log_every = 50
replay_start_size = 2000
train_every = 1
hidden_dims = [64, 64]
activations = ['relu', 'relu', 'linear']
agent = DQNAgent(env.get_state_size(), discount=discount, \
replay_mem_size=replay_mem_size, \
minibatch_size=minibatch_size, epsilon=epsilon, \
# epsilon_decay=epsilon_decay, \
epsilon_min=epsilon_min, \
epsilon_stop_episode=epsilon_stop_episode, \
learning_rate=learning_rate, hidden_dims=hidden_dims, \
activations=activations, \
replay_start_size=replay_start_size)
log_dir = f'log/tetris-{datetime.now().strftime("%Y%m%d-%H%M%S")}-nn={str(hidden_dims)}-mem={replay_mem_size}-bs={minibatch_size}-discount={discount}'
log = ModifiedTensorBoard(log_dir=log_dir)
scores = []
for episode in tqdm(range(episodes)):
current_state = env.reset_game()
done = False
step = 0
log.step = episode
if show_every and episode % show_every == 0:
show = True
else:
show = False
# Run the game until either game over or we've hit max number of steps
while not done and (not max_steps or step < max_steps):
next_states = env.get_next_states()
best_state = agent.best_state(next_states.values())
best_action = None
# action is (x,i), state is [lines_cleared, holes, total_bumpiness, sum_height]
for action, state in next_states.items():
if state == best_state:
best_action = action
break
# reward is the score, done is gameover status
reward, done = env.play_game(best_action[0],
best_action[1],
show=show)
if show:
env.show()
agent.update_replay_memory(current_state, best_action,
next_states[best_action], reward, done)
# move to next timestep
current_state = next_states[best_action]
step += 1
if show:
# After game is completed, collect the final score
print("Episode %d score: %d epsilon: %.2f" %
(episode, env.get_game_score(), agent.epsilon))
scores.append(env.get_game_score())
agent.train(epochs=epochs)
if log_every and episode % log_every == 0:
avg_score = mean(scores[-log_every:])
min_score = min(scores[-log_every:])
max_score = max(scores[-log_every:])
log.update_stats(avg_score=avg_score,
min_score=min_score,
max_score=max_score)
if env.get_game_score() >= MIN_SCORE:
if not os.path.exists('models/'):
os.makedirs('models/')
agent.model.save(
f'models/eps_{str(episode)}nn_{str(hidden_dims)}__bs{minibatch_size}__score_{env.get_game_score()}__{int(time.time())}.h5'
)
def test():
env = gym.make('CartPole-v0')
my_test_agent = DQNAgent(env, model='myClassModel')
avg_reward, max_reward = my_test_agent.test_agent()
print("average reward: ", avg_reward, " maximum reward: ", max_reward)
from flask import Flask, jsonify, request
from DQN import DQNAgent
import os
app = Flask(__name__)
num_state = 6
num_action = 2
### if you want to change the number of node in hidden, you must change it in build_model() in DQN.py and here both.
num_hidden_node = [120, 120]
dqn_agent = DQNAgent(num_state, num_action, num_hidden_node)
@app.route('/model', methods=['GET'])
def get_model():
return jsonify(dqn_agent.get_model())
@app.route('/update', methods=['POST'])
def update():
dqn_agent.run(request.json)
print("finish run")
return jsonify(dqn_agent.get_model())
if __name__ == '__main__':
app.run(debug=False)
trainingEpisodes = 2
testingEpisodes = 2
frames = 1000
name = "DQN"
run = wandb.init(project="test",
config={
"trainingEpisodes": trainingEpisodes,
"testingEpisodes": testingEpisodes,
"frames": frames,
"epsilon": epsilon,
"deepLayers": deepLayers,
"layerSize": layerSize,
"layerSizeMult": layerSizeMult,
"learningRate": learningRate,
"gamma": gamma,
"epsilonDecay": epsilonDecay,
"epsilonMin": epsilonMin,
"batchSize": batchSize,
"memory": memory,
"name": name,
"replay_step_size": replayStepSize,
},
name=name,
allow_val_change=True)
config = wandb.config
agent = DQNAgent(env, config)
trainDQN(DQNAgent)
target_net = NeurosmashAgent()
target_net.load_state_dict(policy_net.state_dict())
target_net.eval()
#Init empty Memorys
memory = ReplayMemory(max_size=1024)
victory_memory = ReplayMemory(max_size=1024)
#Init lists
R = np.zeros(n_episodes)
reward = 0
losses = []
epses = []
#Init DQN agent
agent = DQNAgent(target_net, policy_net, memory)
if torch.cuda.is_available():
print("Running on GPU")
agent.target_net.cuda()
agent.policy_net.cuda()
torch.cuda.empty_cache()
#Catch KeyboardInterrupts and save model
#i=-1
# try:
# #Reinforcement Loop
# #for i in tqdm.trange(n_episodes):
# while True:
# i += 1
for i in range(n_episodes):
info, reward, state = env.reset(
import random
import time
from DQN import DQNAgent
import numpy as np
pygame.init()
pygame.font.init()
pygame.display.set_caption('Snake Game')
window_width = 440
window_height = 480
clock = pygame.time.Clock()
max_score = 0
num_games = 0
game_speed = 10
epsilon = 1
agent = DQNAgent()
class Game:
def __init__(self, window_width, window_height):
self.window_width = 440
self.window_height = 480
self.screen = pygame.display.set_mode((window_width, window_height))
self.background_image = pygame.image.load("images/background.png")
self.score = 0
self.increase_length = False
self.game_over = False
class Player:
def __init__(self, game, x, y, direction=4):
def run():
pygame.init()
agent = DQNAgent()
counter_games = 0
score_plot = []
counter_plot =[]
record = 0
while counter_games < 60:
game = Game(width, height)
pipeU = game.upper_pipe
pipeD = game.lower_pipe
player = game.player
between_pipes = False
while not game.crash:
agent.epsilon = 20 - counter_games
state_old = agent.get_state(game, player, pipeU, pipeD)
if randint(0, 200) < agent.epsilon:
final_move = to_categorical(randint(0, 1), num_classes=2)
else:
# predict action based on the old state
prediction = agent.model.predict(state_old.reshape((1,8)))
final_move = to_categorical(np.argmax(prediction[0]), num_classes=2)
player.Move(game,final_move)
move_pipes(pipeU,pipeD,160)
check_collision(player,pipeU,pipeD,game)
check_score(pipeU,game)
between_pipes = check_pipes(pipeU, pipeD, player)
reward = agent.set_reward(pipeU, game.crash, between_pipes)
state_new = agent.get_state(game, player, pipeU, pipeD)
agent.train_short_memory(state_old, final_move, reward, state_new, game.crash)
# store the new data into a long term memory
agent.remember(state_old, final_move, reward, state_new, game.crash)
record = get_record(game.score, record)
display(game,player,pipeU,pipeD,record)
if game.score == 50:
game.crash = True;
agent.replay_new(agent.memory)
counter_games += 1
print('Game', counter_games, ' Score:', game.score)
score_plot.append(game.score)
counter_plot.append(counter_games)
agent.model.save_weights('weights.hdf5')
plot_seaborn(counter_plot, score_plot)
pygame.quit()
# Exploration settings
epsilon = 1 # not a constant, going to be decayed
# epsilon = 0.00
EPSILON_DECAY = 0.99975
MIN_EPSILON = 0.001
# For more repetitive results
random.seed(1)
np.random.seed(1)
tf.random.set_seed(1)
MIN_REWARD = -200 # For model save
MEMORY_FRACTION = 0.20
agent = DQNAgent()
env = BlobEnv()
# Create models folder
if not os.path.isdir('models'):
os.makedirs('models')
# Iterate over episodes
for episode in tqdm(range(1, EPISODES + 1), ascii=True, unit='episodes'):
# Update tensorboard step every episode
agent.tensorboard.step = episode
# Restarting episode - reset episode reward and step number
episode_reward = 0
step = 1
