def __init__(self, fname):
lr = 0.0005
self.agent = Agent(gamma=0.99,
epsilon=0.0,
alpha=lr,
input_dims=6,
n_actions=2,
mem_size=60000,
batch_size=64,
epsilon_end=0.0,
fname=fname)
self.observation = []
self.action = 0
self.n_step = 0
self.fname = fname.split("/")[-1]
def __init__(self,
bandit,
epsilon,
alpha,
layersize=128,
UI=1000,
gm=0.99,
remember=False,
algorithm='DQNxR'):
self.size = bandit.nvot
if algorithm == 'DQNxR':
seed = np.random.rand() #DOESNT DO ANYTHING
self.DQN = DQNxR(state_size=self.size,
action_size=bandit.N,
seed=seed,
alpha=alpha,
UI=UI,
batch_size=10,
gamma=gm,
tau=1e-3,
buffer_size=int(1e5))
#print(vars(self.DQN))
self.epsilon = epsilon
self.last_state = None
self.remember = remember
elif algorithm == 'policygrad':
self.DQN = None
self.policy = PolicyGrad(state_space=self.size,
action_space=bandit.N,
hidden_layer_size=layersize,
gamma=gm)
self.optimizer = optim.Adam(self.policy.parameters(), lr=alpha)
self.update_interval = UI
self.remember = remember
def main(env, gamma, epsilon, final_epsilon, final_exp_step, lr, memory_size,
target_update_freq, gradient_update_freq, batch_size, replay_start,
val_freq, log_freq_by_step, log_freq_by_ep, val_epsilon, log_dir,
weight_dir, steps):
train_env = make_atari(env + "NoFrameskip-v4")
val_env = make_atari(env + "NoFrameskip-v4", noop=False)
agent = Agent(train_env,
DQN,
gamma=gamma,
epsilon=epsilon,
final_epsilon=final_epsilon,
final_exp_step=final_exp_step)
trainer = Trainer(agent,
val_env,
lr=lr,
memory_size=memory_size,
target_update_freq=target_update_freq,
gradient_update_freq=gradient_update_freq,
batch_size=batch_size,
replay_start=replay_start,
val_freq=val_freq,
log_freq_by_step=log_freq_by_step,
log_freq_by_ep=log_freq_by_ep,
val_epsilon=val_epsilon,
log_dir=log_dir,
weight_dir=weight_dir)
trainer.train(steps)
def main():
agent = Agent()
agent.load()
total_reward = 0
obs = env.reset()
env.render()
for _ in range(10000):
act = agent.predict(obs)
obs, reward, done, _ = env.step(act)
total_reward += reward
env.render()
if done:
print(f'total_reward: {total_reward}')
env.close()
break
def setup_Agent(filename, epsilon):
"""
Function to initialize the DQN agent
"""
# one hot vector (opponents move) on top of game board
input_dims = 7 * 7
action_space = tuple(range(7))
n_actions = 7
h1_dims = 512
h2_dims = 256
agent = Agent(lr=0.001,
gamma=0.95,
epsilon=epsilon,
epsilon_dec=0.995,
epsilon_min=0.01,
input_shape=input_dims,
h1_dims=h1_dims,
h2_dims=h2_dims,
action_space=action_space,
training_epochs=2,
fname=filename)
memory = ReplayBuffer(50000, input_dims, n_actions)
return agent, memory
def main(env_name=None):
ENV_NAME = 'wumpus-v0'
if env_name: ENV_NAME = env_name
MODEL_DIR = f'models/{ENV_NAME}-dqn'
MODEL_FILE = f'{ENV_NAME}-dqn.h5'
CHECKPOINTS_DIR = f'models/{ENV_NAME}-dqn/checkpoints'
TEST_IMG_DIR = f'tests/{ENV_NAME}-dqn'
env = gym.make(ENV_NAME)
env.reset()
agent = Agent(learning_rate=0.01, gamma=0.95,
state_shape=env.observation_space.shape, actions=7,
batch_size=64,
epsilon_initial=0.0, epsilon_decay=0, epsilon_final=0.0,
replay_buffer_capacity=1000000,
model_name=MODEL_FILE, model_dir=MODEL_DIR,
ckpt_dir=CHECKPOINTS_DIR)
agent.load_model()
done = False
score = 0
steps_per_episode = 0
state = env.reset()
images = [env.render('rgb_array')]
while not done:
# Choose action according to policy, and execute
action = agent.select_action(state)
state, reward, done, _ = env.step(action)
score += reward
steps_per_episode += 1
images.append(env.render('rgb_array'))
# Generate GIF for the execution
create_gif(
f'{ENV_NAME}.gif',
np.array(images),
fps=1.0
)
print(
f'Model \'{str(ENV_NAME)}\', score {score}, steps {steps_per_episode}')
class AI:
def __init__(self, fname):
lr = 0.0005
self.agent = Agent(gamma=0.99,
epsilon=0.0,
alpha=lr,
input_dims=6,
n_actions=2,
mem_size=60000,
batch_size=64,
epsilon_end=0.0,
fname=fname)
self.observation = []
self.action = 0
self.n_step = 0
self.fname = fname.split("/")[-1]
def episode_start(self, observation):
self.observation = observation
def choose_action(self):
self.action = self.agent.choose_action(self.observation)
return self.action
def step(self, observation_, reward, done):
self.agent.remember(self.observation, self.action, reward,
observation_, int(done))
self.observation = observation_
if self.n_step % 3 == 0:
self.agent.learn()
self.n_step += 1
def episode_end(self):
self.agent.save_model()
def test_agent():
print("##################Running agent test##################")
agent = Agent(state_shape, action_shape)
state1 = np.array([1,2,3,4]).reshape(-1,4)
state2 = np.array([2,3,4,5]).reshape(-1,4)
out1 = agent.model.predict(state1)
out2 = agent.model.predict(state2)
print(out1)
print(out2)
assert np.all((out1[0]-out2[0])!=0), "Failed agent test - same output different state"
print("Agent test passed :)\n\n")
def __init__(self):
self._load_config()
# Control parameter used to scale bid price
self.BETA = [-0.08, -0.03, -0.01, 0, 0.01, 0.03, 0.08]
self.eps_start = 0.95
self.eps_end = 0.05
self.anneal = 0.00005
self._reset_episode()
# DQN Network to learn Q function
self.dqn_agent = Agent(state_size=7, action_size=7, seed=0)
# Reward Network to reward function
self.reward_net = RewardNet(state_action_size=8, reward_size=1, seed=0)
self.dqn_state = None
self.dqn_action = 3 # no scaling
self.dqn_reward = 0
# Reward-Dictionary
self.reward_dict = {}
self.S = []
self.V = 0
self.total_wins = 0
self.total_rewards = 0.0
def main():
gym_env = gym.make('custom_gym:Xplane-v0')
lr = 0.001
gam = 0.01
n_games = 1
# nn_input = obs()
agent = Agent(learning_rate=lr,
gamma=gam,
epsilon=1.0,
input_dims=(6, ),
n_actions=15,
batch_size=32,
file_name='AI_takeoff/saved_models/dq_model_2.h5')
scores = []
total_steps = []
eps_hist = []
agent.load_model()
for i in range(n_games):
try:
done = False
score = 0
observation = gym_env.reset()
time.sleep(2)
observation_checkpoints = np.array([observation[0:2]])
step_counter = 0
print("GAME ITERATION ", i)
while not done:
action = agent.choose_action(observation)
new_observation, reward, done = gym_env.step(action)
step_counter = step_counter + 1
score = score + reward
agent.store_transition(observation, action, reward,
new_observation, done)
observation = new_observation
# agent.learn()
# This if statement checks if the airplane is stuck
observation_checkpoints = np.append(observation_checkpoints,
[new_observation[0:2]],
axis=0)
print(observation_checkpoints)
print("stepcounter is", step_counter)
if step_counter % 30 == 0:
if np.array_equal(
observation_checkpoints[step_counter - 30],
observation_checkpoints[step_counter - 1]):
done = True
eps_hist.append(agent.epsilon)
scores.append(score)
total_steps.append(step_counter)
except Exception as e:
print(str(e))
def main():
#make env and agent
env = gym.make('LunarLander-v2')
agent = Agent(gamma=0.99,
epsilon=1.0,
batch_size=64,
n_actions=4,
eps_end=0.01,
input_dims=[8],
lr=0.0001)
scores, eps_history = [], []
n_games = 500
for i in range(n_games):
score = 0
done = False
observation = env.reset()
while not done:
#ingame
#get action from current view of game (observation)
action = agent.choose_action(observation)
#next frame
observation_, reward, done, info = env.step(action)
score += reward
#store memory
agent.store_transisation(observation, action, reward, observation_,
done)
agent.learn()
#set next stage to current stage
observation = observation_
#append score and eps
scores.append(score)
eps_history.append(agent.epsilon)
#print some nice statements
avg_score = np.mean(scores[-100:])
print(
f'Episode: {i} Score: {score} Average Score: {avg_score} Epsilon: {agent.epsilon}'
)
def start():
env = gym.make('CartPole-v0')
params = {
'gamma': 0.8,
'epsi_high': 0.9,
'epsi_low': 0.05,
'decay': 500,
'lr': 0.001,
'capacity': 10000,
'batch_size': 64,
'state_space_dim': env.observation_space.shape[0],
'action_space_dim': env.action_space.n
}
agent = Agent(**params)
score = []
mean = []
for episode in range(1000):
s0 = env.reset()
total_reward = 1
for i in range(200):
env.render()
a0 = agent.act(s0)
s1, r1, done, _ = env.step(a0)
if done:
r1 = -1
agent.put(s0, a0, r1, s1)
if done:
break
total_reward += r1
s0 = s1
agent.learn()
score.append(total_reward)
mean.append(sum(score[-100:]) / 100)
print(total_reward)
def OldStuff():
tf.compat.v1.disable_eager_execution()
lr = 0.001
numGames = 10000
session = TriadGameSession()
observation = session.getState()
scores = []
agent = Agent(gamma=0.99,
lr=lr,
epsilon=1.0,
epsilonDec=0.0005,
inputSize=[len(observation)],
numActions=session.getMaxActions(),
memSize=1000000,
batchSize=1024)
for i in range(numGames):
done = False
score = 0
session = TriadGameSession()
observation = session.getState()
while not done:
action = agent.chooseAction(observation)
observationNext, reward, done = session.step(action)
score += reward
agent.store(observation, action, reward, observationNext, done)
observation = observationNext
agent.learn()
scores.append(score)
avgScore = np.mean(scores[-100:])
print('game:', i, 'score %.2f' % score, 'avgScore %.2f' % avgScore,
'epsilon %.2f' % agent.epsilon)
#agent.save()
print('Finished!')
def setup_Agent(filename, epsilon):
"""
Function to initialize the DQN agent
"""
input_dims = 6 * 7
action_space = tuple(range(7))
n_actions = 7
h1_dims = 512
h2_dims = 256
agent = Agent(lr=0.001,
gamma=0.95,
epsilon=epsilon,
epsilon_dec=0.995,
epsilon_min=0.01,
input_shape=input_dims,
h1_dims=h1_dims,
h2_dims=h2_dims,
action_space=action_space,
training_epochs=1,
fname=filename)
return agent
state_size = 122
action_size = 5
# Add some variables to keep track of the progress
scores_window, steps_window = [deque(maxlen=200) for _ in range(2)
] # a, b = [deque([]), deque([])]
agent_obs = [None] * flags.num_agents # [None, None]
agent_obs_buffer = [None] * flags.num_agents
agent_action_buffer = [2] * flags.num_agents
max_steps = flags.episode_length
start_time = time.time()
# Load an RL agent and initialize it from checkpoint if necessary
# independent dqn/ppo -->每个人obs不同，同一个model
if flags.agent_type == "dqn":
agent = DQN_Agent(state_size, action_size, flags.num_agents)
elif flags.agent_type == "ppo":
agent = PPO_Agent(state_size, action_size, flags.num_agents)
if flags.load_model:
start, eps = agent.load(project_root / 'checkpoints', 0, 1.0)
else:
start, eps = 0, 1
if not flags.train:
eps = 0.0
# Helper function to detect collisions
ACTIONS = {0: "up", 1: "right", 2: "down", 3: "left", 4: "stop"}
import gym
from dqn import DeepQNetwork, Agent
import numpy as np
from gym import wrappers
if __name__ == '__main__':
env = gym.make('LunarLander-v2')
brain = Agent(gamma=0.99,
epsilon=1.0,
n_actions=4,
batch_size=128,
input_dims=[8],
alpha=0.0003,
replace=64)
scores = []
eps_history = []
num_games = 500
score = 0
for i in range(num_games):
if i % 10 == 0 and i > 0:
avg_score = np.mean(scores[max(0, i - 10):(i + 1)])
print('episode: ', i, 'score: ', score,
' average score %.3f' % avg_score,
'epsilon %.3f' % brain.EPSILON)
else:
print('episode: ', i, 'score: ', score)
eps_history.append(brain.EPSILON)
done = False
observation = env.reset()
import sys
import gym
from dqn import Agent
num_episodes = 5000
env_name = sys.argv[1] if len(sys.argv) > 1 else "MsPacman-v0"
env = gym.make(env_name)
agent = Agent(state_size=env.observation_space.shape,
number_of_actions=env.action_space.n,
save_name=env_name)
for e in xrange(num_episodes):
observation = env.reset()
done = False
agent.new_episode()
total_cost = 0.0
total_reward = 0.0
frame = 0
while not done:
frame += 1
#env.render()
action, values = agent.act(observation)
#action = env.action_space.sample()
observation, reward, done, info = env.step(action)
total_cost += agent.observe(reward)
total_reward += reward
print "total reward", total_reward
print "mean cost", total_cost / frame
# ------------------------------ Variable Declaration ----------------------------------
NUM_OF_ZOMBIES = 1
NUM_OF_VILLAGERS = 1
agent_host = MalmoPython.AgentHost()
malmoutils.parse_command_line(agent_host)
validate = True
num_reps = 300
#=======core part initialization====================================
#input size 5*5, you can change the size here
memory = MemoryD(5)
network_model, q_values_func = nn_model(input_shape=[5, 5])
agent = Agent(network_model, q_values_func, memory, 'train', 'ddqn')
#set learning rate to be 0.00025
agent.do_compile(optimizer=Adam(lr=0.00025), loss_func=mean_huber_loss)
agent.memoryD.clear()
#===================================================================
for iRepeat in range(num_reps):
my_mission_record = malmoutils.get_default_recording_object(
agent_host, "./Mission_{}".format(iRepeat + 1))
#my_mission_record = MalmoPython.MissionRecordSpec('./' + "Mission_" + str(iRepeat) + ".tgz")
#my_mission_record.recordRewards()
#my_mission_record.recordMP4(24,400000)
#my_mission_record.recordObservations()
my_mission = MalmoPython.MissionSpec(GetMissionXML(mapblock, agent_host),
validate)
scores = []
epsHistory = []
numGames = 1
batch_size = 32
n_actions = 6
input_dims = (185, 95)
crop_start = (15, 30)
crop_end = (200, 125)
starting_epsilon = 0.05 if LOAD_MODEL else 1.0
env = gym.make('SpaceInvaders-v0')
brain = Agent(gamma=0.95,
epsilon=0.05,
lr=0.003,
input_dims=input_dims,
batch_size=batch_size,
n_actions=n_actions,
max_mem_size=5000,
save_path='models/')
if LOAD_MODEL:
brain.load()
else:
# load memory with random games
while brain.mem_cntr < brain.mem_size:
observation = env.reset()
observation = preprocess(observation, crop_start, crop_end)
done = False
while not done:
# 0 no action, 1 fire, 2 move right, 3 move left, 4 move right fire, 5 move left fire
action = env.action_space.sample()
import gym
from dqn import Agent
# Python 3 compatability
try:
xrange
except NameError:
xrange = range
num_episodes = 20
env_name = sys.argv[1] if len(sys.argv) > 1 else "MsPacman-v0"
env = gym.make(env_name)
agent = Agent(state_size=env.observation_space.shape,
number_of_actions=env.action_space.n,
save_name=env_name)
for e in xrange(num_episodes):
observation = env.reset()
done = False
agent.new_episode()
total_cost = 0.0
total_reward = 0.0
frame = 0
while not done:
frame += 1
#env.render()
action, values = agent.act(observation)
#action = env.action_space.sample()
observation, reward, done, info = env.step(action)
def train(path, env):
#env = Monitor(env, path, video_callable=video_callable, force=True)
agent = Agent(env, path=path)
agent.train()
return agent
import gym
from dqn import Agent
from utils import PlotLearning
import numpy as np
if __name__ == '__main__':
env = gym.make('LunarLander-v2')
agent = Agent(gamma=0.99,
epsilon=1.0,
batch_size=64,
n_actions=4,
eps_end=0.01,
inp_dims=[8],
lr=0.001)
scores, eps_history = [], []
n_games = 500
for i in range(n_games):
score = 0
done = False
observation = env.reset()
while not done:
action = agent.choose_action(observation)
observation_, reward, done, info = env.step(action)
score += reward
agent.store_transition(observation, action, reward, observation_,
done)
agent.learn()
observation = observation_
scores.append(score)
eps_history.append(agent.epsilon)
def run(env: LlvmEnv) -> None:
agent = Agent(n_actions=15, input_dims=[69])
env.observation_space = "InstCountNorm"
agent.Q_eval.load_state_dict(torch.load("./H10-N4000-INSTCOUNTNORM.pth"))
rollout(agent, env)
from dqn import Agent
import numpy as np
import gym
import matplotlib.pyplot as plt
if __name__ == '__main__':
env = gym.make('LunarLander-v2')
n_games = 300
show = False
agent = Agent(gamma=0.99, epsilon=1.0, alpha=0.0005, input_dims=8,
n_actions=4, batch_size=64)
scores = []
eps_history = []
for i in range(1, n_games+1):
done = False
score = 0
obseervation = env.reset()
while not done:
if show:
env.render()
action = agent.choose_action(obseervation)
obseervation_, reward, done, info = env.step(action)
score += reward
agent.remember(obseervation, action, reward, obseervation_, done)
obseervation = obseervation_
agent.learn()
eps_history.append(agent.epsilon)
def main():
scores = []
eps_history = []
info_history = []
# Random starting-points:
env = sky.make(random=True,
xi=(301, 650 - 25),
yi=(100, 300 - 25),
width=15,
height=15,
v_initial=14)
# Fixed starting-point:
#env = sky.make(xi=550)
agent = Agent(gamma=gamma,
epsilon=epsilon,
lr=lr,
input_dims=[imput_dimensions],
n_actions=n_actions,
mem_size=mem_size,
batch_size=batch_size,
epsilon_dec=epsilon_dec)
if (load_checkpoint):
agent.load_modes()
for i in range(n_games):
score = 0
done = False
observation = env.reset()
while not done:
'''
one game: ending, when done=True
'''
action = agent.choose_action(observation)
observation_, reward, done, info = env.step(action)
score += reward
agent.store_transition(observation, action, reward, observation_,
int(done))
observation = observation_
agent.learn()
if i % 10 == 0 and i > 0:
avg_score = np.mean(scores[max(0, i - 10):(i + 1)])
print(i, 'episode', info, '|| score:', score,
'| average score: %.3f' % avg_score,
'| epsilon: %.3f' % agent.epsilon, '| training done:',
round(i / n_games, 2))
else:
print(i, 'episode', info, '|| score:', score)
scores.append(score)
eps_history.append(agent.epsilon)
info_history.append(info)
print('training ended with:',
[[el, info_history.count(el)] for el in ('crashed', 'goal')])
if (save_checkpoint):
agent.save_models()
print('[+] model saved')
# -------------------
# Plotting and output
# -------------------
x = [i + 1 for i in range(n_games)]
# First axis: Scores
fig, ax1 = plt.subplots()
color = 'tab:red'
ax1.set_xlabel('Episode')
ax1.set_ylabel('score per Episode', color=color)
ax1.scatter(x, scores, color=color, s=2)
ax1.tick_params(axis='y', labelcolor=color)
# Second axis: epsilon
ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
color = 'tab:blue'
ax2.set_ylabel('epsilon',
color=color) # we already handled the x-label with ax1
ax2.plot(x, eps_history, color=color)
ax2.tick_params(axis='y', labelcolor=color)
# Output
fig.tight_layout() # otherwise the right y-label is slightly clipped
plt.savefig(filename)
return env
def main():
# Initialize environment, agent
env = gym.make(ENV_NAME)
summary_writer = tf.summary.create_file_writer(LOG_DIR)
agent = Agent(learning_rate=0.01, gamma=0.95,
state_shape=env.observation_space.shape, actions=7,
batch_size=64,
epsilon_initial=0.9, epsilon_decay=1e-6, epsilon_final=0.01,
replay_buffer_capacity=1000000,
model_name=MODEL_FILE, model_dir=MODEL_DIR,
ckpt_dir=CHECKPOINTS_DIR, log_dir=LOG_DIR)
scores = []
for i in range(1, EPISODES + 1):
done = False
score = 0
state = env.reset()
steps_per_episode = 0
# Play one episode
while not done:
# Choose action (epsilon greedy), and execute
action = agent.select_action(state)
next_state, reward, done, _ = env.step(action)
score += reward
# Store in experience replay buffer
agent.store_experience(state, action,
reward, next_state, done)
state = next_state
agent.train()
steps_per_episode += 1
if len(scores) == 100:
scores.pop(0)
scores.append(score)
avg_score = np.mean(scores)
min_score = np.min(scores)
max_score = np.max(scores)
print(
f'Episode: {i}, Score {score:.2f}, Avg_score {avg_score:.2f}, Epsilon {agent.epsilon:.2f}')
# Summaries for Tensorboard
write_summaries(summary_writer, {
'epsilon': agent.epsilon,
'reward.episode': score,
'reward.avg': avg_score,
'reward.min': min_score,
'reward.max': max_score,
'steps.count': steps_per_episode
}, i, ENV_NAME)
# Save the model
if i % SAVE_INTERVAL == 0:
print(f'Saving model to \'{MODEL_FILE}\' [Overwriting]')
agent.save_model()
# Save checkpoint
if i % CHECKPOINT_INTERVAL == 0:
print(f'Adding checkpoint: \'{CHECKPOINTS_DIR}/episode-{i}.h5\'')
agent.save_checkpoint(f'episode-{i}')
out2 = agent.model.predict(state2)
print(out1)
print(out2)
assert np.all((out1[0]-out2[0])!=0), "Failed agent test - same output different state"
print("Agent test passed :)\n\n")
state_shape=env.observation_space.shape # the state space
action_shape=env.action_space.n # the action space
#Testing Memory storage and sample
state = env.reset()
test_mem()
test_agent()
mem = Memory(10000, state_shape)
agent = Agent(state_shape, action_shape)
epsilon = 1
batch_size = 64
#action = env.action_space.sample()
#next_state, reward, done, info = env.step(action)
for game in range(n_games):
state = env.reset()
game_reward = 0
for step in range(max_steps):
#Render game
if game % 10 == 0:
class RlBidAgent():
def _load_config(self):
"""
Parse the config.cfg file
"""
cfg = configparser.ConfigParser(allow_no_value=True)
env_dir = os.path.dirname(__file__)
cfg.read(env_dir + '/config.cfg')
self.budget = int(cfg['agent']['budget'])
self.target_value = int(cfg['agent']['target_value'])
self.T = int(cfg['rl_agent']['T']) # T number of timesteps
self.STATE_SIZE = int(cfg['rl_agent']['STATE_SIZE'])
self.ACTION_SIZE = int(cfg['rl_agent']['ACTION_SIZE'])
def __init__(self):
self._load_config()
# Control parameter used to scale bid price
self.BETA = [-0.08, -0.03, -0.01, 0, 0.01, 0.03, 0.08]
self.eps_start = 0.95
self.eps_end = 0.05
self.anneal = 0.00005
self._reset_episode()
# DQN Network to learn Q function
self.dqn_agent = Agent(state_size=7, action_size=7, seed=0)
# Reward Network to reward function
self.reward_net = RewardNet(state_action_size=8, reward_size=1, seed=0)
self.dqn_state = None
self.dqn_action = 3 # no scaling
self.dqn_reward = 0
# Reward-Dictionary
self.reward_dict = {}
self.S = []
self.V = 0
self.total_wins = 0
self.total_rewards = 0.0
def _reset_episode(self):
"""
Function to reset the state when episode changes
"""
self.t_step = 0 # 1. t: the current time step
self.budget_spend = 0.0
self.rem_budget = self.budget # 2. the remaining budget at time-step t
self.ROL = self.T # 3. the number of Lambda regulation opportunities left
self.prev_budget = self.budget # Bt-1
self.BCR = 0 # 4. Budget consumption rate
# (self.budget - self.prev_budget) / self.prev_budget
self.CPM = 0 # 5. Cost per mille of impressions between t-1 and t
# (self.prev_budget - self.running_budget) / self.cur_wins
self.WR = 0 # 6. wins_e / total_impressions
self._reset_step(
) # 7. Total value of the winning impressions 'click_prob'
self.cur_day = 1
self.cur_hour = 0
self.ctl_lambda = 1.0 # Lambda sequential regulation parameter
self.wins_e = 0
self.eps = self.eps_start
self.V = 0
def _update_step(self):
"""
Function to call to update the state with every bid request
received for the state modeling
"""
self.t_step += 1
self.prev_budget = self.rem_budget
self.rem_budget -= (self.cost_t / 1e9)
self.ROL -= 1
self.BCR = (self.rem_budget - self.prev_budget) / self.prev_budget
self.CPM = self.cost_t
self.WR = self.wins_t / self.bids_t
def _reset_step(self):
"""
Function to call every time a new time step is entered.
"""
self.reward_t = 0.
self.cost_t = 0.
self.wins_t = 0
self.bids_t = 0
self.eps = max(self.eps_start - self.anneal * self.t_step, 0.05)
def _update_reward_cost(self, reward, cost):
"""
Internal function to update reward and action to compute the cumulative
reward and cost within the given step.
"""
self.reward_t += reward
self.cost_t += cost
self.bids_t += 1
self.total_rewards += reward
def _get_state(self):
"""
Returns the state that will be used for the DQN state.
"""
return np.asarray([
self.t_step, self.rem_budget, self.ROL, self.BCR, self.CPM,
self.WR, self.reward_t
])
def act(self, state, reward, cost):
"""
This function gets called with every bid request.
By looking at the weekday and hour to progress between the steps and
episodes during training.
Returns the bid request cost based on the scaled version of the
bid price using the DQN agent output.
"""
episode_done = (state['weekday'] != self.cur_day)
# within the time step
if state['hour'] == self.cur_hour and state['weekday'] == self.cur_day:
self._update_reward_cost(reward, cost)
# within the episode, changing the time step
elif state['hour'] != self.cur_hour and state[
'weekday'] == self.cur_day:
self._update_step()
# Sample a mini batch and perform grad-descent step
self.reward_net.step()
dqn_next_state = self._get_state()
a_beta = self.dqn_agent.act(dqn_next_state, eps=self.eps)
sa = np.append(self.dqn_state, self.dqn_action)
rnet_r = float(self.reward_net.act(sa))
# call agent step
self.dqn_agent.step(self.dqn_state, self.dqn_action, rnet_r,
dqn_next_state, episode_done)
self.dqn_state = dqn_next_state
self.dqn_action = a_beta
# print(dqn_next_state, a_beta)
self.ctl_lambda *= (1 + self.BETA[a_beta])
self.cur_hour = state['hour']
self._reset_step()
self._update_reward_cost(reward, cost)
self.V += self.reward_t
self.S.append((self.dqn_state, self.dqn_action))
# episode changes
elif state['weekday'] != self.cur_day:
for (s, a) in self.S:
sa = tuple(np.append(s, a))
max_r = max(self.reward_net.get_from_M(sa), self.V)
self.reward_net.add_to_M(sa, max_r)
self.reward_net.add(sa, max_r)
print("Total Impressions won with Budget={} Spend={} wins = {}".
format(self.budget, self.budget_spend, self.wins_e))
self.total_wins += self.wins_e
self._reset_episode()
self.cur_day = state['weekday']
self.cur_hour = state['hour']
self._update_reward_cost(reward, cost)
# action = bid amount
# send the best estimate of the bid
self.budget_spend += (cost / 1e9)
if cost > 0:
self.wins_t += 1
self.wins_e += 1
action = min(
self.ctl_lambda * self.target_value * state['click_prob'] * 1e9,
(self.budget - self.budget_spend) * 1e9)
return action
def done(self):
return self.budget <= self.budget_spend
class Neural_Agent:
def __init__(self,
bandit,
epsilon,
alpha,
layersize=128,
UI=1000,
gm=0.99,
remember=False,
algorithm='DQNxR'):
self.size = bandit.nvot
if algorithm == 'DQNxR':
seed = np.random.rand() #DOESNT DO ANYTHING
self.DQN = DQNxR(state_size=self.size,
action_size=bandit.N,
seed=seed,
alpha=alpha,
UI=UI,
batch_size=10,
gamma=gm,
tau=1e-3,
buffer_size=int(1e5))
#print(vars(self.DQN))
self.epsilon = epsilon
self.last_state = None
self.remember = remember
elif algorithm == 'policygrad':
self.DQN = None
self.policy = PolicyGrad(state_space=self.size,
action_space=bandit.N,
hidden_layer_size=layersize,
gamma=gm)
self.optimizer = optim.Adam(self.policy.parameters(), lr=alpha)
self.update_interval = UI
self.remember = remember
#POLICY GRADIENT
def select_action(self, state):
#Select an action (0 or 1) by running policy model and choosing based on the probabilities in state
state = torch.from_numpy(state).type(torch.FloatTensor)
state = self.policy(Variable(state))
c = Categorical(state)
action = c.sample()
# Add log probability of our chosen action to our history
if self.policy.policy_history.dim() != 0:
#print(policy.policy_history)
#print(c.log_prob(action))
self.policy.policy_history = torch.cat(
[self.policy.policy_history,
c.log_prob(action).unsqueeze(0)])
#print("DID!")
else:
self.policy.policy_history = (c.log_prob(action))
return action
def update_policy(self):
R = 0
rewards = []
#print(self.policy.reward_episode)
# Discount future rewards back to the present using gamma
for r in self.policy.reward_episode[::-1]:
R = r + self.policy.gamma * R
rewards.insert(0, R)
# Scale rewards
rewards = torch.FloatTensor(rewards)
rewards = (rewards - rewards.mean()) / (rewards.std() +
np.finfo(np.float32).eps)
# Calculate loss
loss = (torch.sum(
torch.mul(self.policy.policy_history, Variable(rewards)).mul(-1),
-1))
# Update network weights
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
#self.policy.loss_history.append(loss.data.item())
#self.policy.reward_history.append(np.sum(policy.reward_episode))
self.policy.policy_history = Variable(torch.Tensor())
self.policy.reward_episode = []
#UNIVERSAL
def update_Q(self, action, reward):
if self.DQN is not None:
self.AR = (action, reward)
else:
if len(self.policy.reward_episode) == self.update_interval:
self.policy.reward_episode.append(reward)
self.update_policy()
else:
self.policy.reward_episode.append(reward)
def get_action(self, bandit, actnum, decline, N_episodes):
if self.remember == False:
state = np.ones(self.size) / 100
elif self.remember == "Rewards":
state_info = bandit.last_rewards
state = np.array(state_info)
#print(actnum, state)
elif self.remember == "Actions":
state_info = bandit.last_actions
state = np.array(state_info)
elif self.remember == "Actions_now":
state = bandit.partial_result
if self.DQN is not None:
if self.last_state is not None:
#print(actnum,self.last_state,self.AR[0],self.AR[1],state)
self.DQN.step(self.last_state,
self.AR[0],
self.AR[1],
state,
done=False)
#print(self.last_state,self.AR[0],self.AR[1],state)
actnum = self.DQN.act(state, self.epsilon).item()
self.last_state = state
else:
actnum = self.select_action(state).item()
#print(state, actnum)
return actnum
def main(argv):
# Set seeds
np.random.seed(FLAGS.seed)
t.manual_seed(FLAGS.seed)
# Create logfile
f = create_exp_logfile(os.path.join(FLAGS.exp_log_dir, str(FLAGS.learning_rate), str(FLAGS.seed)))
# Initialise agent and environment
env = LunarLander()
num_actions = env.num_actions()
agent = Agent(body_type='ff',
obs_num_features_or_obs_in_channels=FLAGS.observation_dimensions,
fc_hidden_layer_size = FLAGS.fc_hidden_layer_size,
output_actions = num_actions,
use_target_net = FLAGS.use_target_net,
g = FLAGS.gamma,
lr = FLAGS.learning_rate)
# Initialise data structures
c_buf = CircularBuffer(size=FLAGS.cb_size)
er_buf = ExperienceReplayBuffer(size=FLAGS.er_size, batch_size=FLAGS.batch_size)
# Initialise sampling range for e-greedy
interval = t.distributions.uniform.Uniform(t.tensor([0.0]), t.tensor([1.0]))
# Run
step = 0
episode_results = []
state = env.reset()
c_buf.append(t.from_numpy(state).float())
while step < FLAGS.max_steps:
# Agent select action
eps = max(FLAGS.init_epsilon - (((FLAGS.init_epsilon - FLAGS.final_epsilon) / FLAGS.epsilon_anneal) * step), FLAGS.final_epsilon)
if interval.sample() <= eps:
action = np.random.randint(num_actions)
else:
action = agent.greedy_action(c_buf()).item()
reward, next_state, terminal = env.act(action)
terminal = 1 if terminal else 0
er_buf.append(state, action, reward, next_state, terminal)
state = next_state
c_buf.append(t.from_numpy(state).float())
if step > FLAGS.batch_size and step % FLAGS.update_frequency:
batch_states, batch_actions, batch_rewards, batch_next_states, batch_terminals = \
er_buf.sample()
batch_states = t.from_numpy(batch_states).float()
batch_actions = np.array(batch_actions)
batch_rewards = np.array(batch_rewards)
batch_next_states = t.from_numpy(batch_next_states).float()
batch_terminals = np.array(batch_terminals)
agent.optimise(batch_states, batch_actions, batch_rewards, batch_next_states, batch_terminals)
if step % FLAGS.target_network_update == 0:
agent.sync()
if terminal:
episode_results.append(env.episode_return())
state = env.reset()
step += 1
if step % FLAGS.evaluate == 0:
f.write('{}, {}\n'.format(step, performance_avg(episode_results, FLAGS.num_episodes_average)))
f.flush()
f.close()
import gym
from keras.models import load_model
from dqn import Agent
env_name = 'CartPole-v0'
eps = 0.8
episodes = 5
env = gym.make(env_name)
model = load_model('./model/my_model.h5')
agent = Agent(env)
for episode in range(episodes):
# initial state
s = env.reset()
done = False
while not done:
for i in range(50):
a = agent.act(s, eps)
env.render(a)
s2, r, done, info = env.step(a)
s = s2
env.close()
