def pl_grad():
print("hi")
environment = gym.make("CartPole-v1")
net = nn.Sequential(nn.Linear(4, 40, bias=False), nn.ReLU(),
nn.Linear(40, 2, bias=False), nn.Softmax(dim=1))
class distributionNet(nn.Module):
def __init__(self):
super(distributionNet, self).__init__()
self.net = net
def forward(self, x):
return Categorical(self.net(x))
a_model = distributionNet()
optimizer = torch.optim.Adam(a_model.parameters(), lr=0.01)
learner = PolicyGradient(environment,
a_model,
optimizer,
discount_factor=0.99)
opt_policy, history = learner.learn_policy(epochs=500,
episodes_per_update=1)
plt.plot(history)
plt.xlabel('episode')
plt.ylabel('total reward')
plt.savefig("score.png")
agent = Agent(environment=environment, policy=opt_policy)
input("add anything to continue")
agent.perform_episode(render=True)
def main():
"""
This function will be called for training phase.
"""
# Sample code for illustration, add your code below to run in test phase.
# Load trained model from train/ directory
env = gym.make(MINERL_GYM_ENV)
if FRAME_SKIP > 0:
env = FrameSkip(env, enable_rendering=True)
env = ObsWrapper(env)
env = MoveAxisWrapper(env, -1, 0)
env = CombineActionWrapper(env)
agent = Agent(env.observation_space, env.action_space)
agent.load_model()
for _ in range(MINERL_MAX_EVALUATION_EPISODES):
obs = env.reset()
done = False
netr = 0
while not done:
action = agent.act(obs)
obs, reward, done, info = env.step(action)
netr += reward
env.render()
env.close()
def plot_grid_2_mc():
test_grids = TEST_GRIDS
all_test_list = [(key, grid) for key, grid in test_grids.items()]
sorted(all_test_list, key=lambda x: x[0])
agent = Agent()
iters = ITERS
total_normal_grid_score, total_grid1_score, total_grid2_score, total_grid3_score, total_grid4_score = [],[],[],[],[]
repeats = REPEATS
# for n in iters:
# print("Running iteration {n}".format(n=n))
grid2_score, grid4_score = [], []
for ind, grid_init in all_test_list:
normalized_score = 0
for j in range(repeats):
grid_num = int(ind) #ind initially is a string.
if (grid_num < 200) or (grid_num > 300):
continue
best_reward = grid_init['best_reward']
testgrid = Grid(5, random=False, init_pos=grid_init)
if grid_num in {204, 208}:
Q, policy = agent.mc_first_visit_control(testgrid.copy(),
iters=500)
_, _, mc_reward = agent.run_final_policy(testgrid.copy(),
Q,
display=True)
else:
continue
normalized_score += mc_reward - best_reward
if normalized_score != 0:
print(
"Grid num {0} did not achieve best score".format(grid_num))
def main():
print ("note: 'ulimit -Sn 1024' if Errno 24")
parser = argparse.ArgumentParser()
parser.add_argument('--env', default='CartPole-v1')
parser.add_argument('--seed', type=int, default=417)
parser.add_argument('--n-timesteps', type=int, default=1e5)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--max-kl', type=float, default=1e-2)
parser.add_argument('--log-interval', type=int, default=1e4)
parser.add_argument('--save-path', default=None)
parser.add_argument('--batch-size', type=int, default=1)
parser.add_argument('--cuda', type=bool, default=False)
parser.add_argument('--update-rule', default='A2C')
args = parser.parse_args()
if args.cuda:
assert torch.cuda.is_available(), 'No available cuda devices'
envs = [gym.make(args.env) for _ in range(args.batch_size)]
set_seeds(envs, args.seed, args.cuda)
agent = Agent(envs[0].observation_space, envs[0].action_space)
if args.cuda:
agent.cuda()
rets = learn(agent, envs, args.update_rule, cuda=args.cuda, n_timesteps=args.n_timesteps, gamma=args.gamma,
log_interval=args.log_interval, max_kl=args.max_kl)
torch.save(rets, "./out/{}_{}".format(args.env, args.update_rule))
if not (args.save_path is None):
torch.save(agent.state_dict(), args.save_path)
def __init__(self,
config,
agent=None,
save_path=None,
restore_dir=None,
device="cpu"):
self.config = config
self.train_config = config["training"]
self.input_config = config["input"]
self.best_metric = 0
self.save_path = save_path
self._matrix = DataMatrices.create_from_config(config)
self.time_index = self._matrix._DataMatrices__global_data.time_index.values
self.coins = self._matrix._DataMatrices__global_data.coins.values
self.test_set = self._matrix.get_test_set()
self.training_set = self._matrix.get_training_set()
tf.random.set_seed(self.config["random_seed"])
self.device = device
self._agent = Agent(config,
time_index=self.time_index,
coins=self.coins,
restore_dir=restore_dir)
self.keras_test = self._matrix.keras_batch(data="test")
def run(load_path, model_path, index, load_model):
with tf.device('/gpu:0'):
trainer = tf.train.AdamOptimizer(learning_rate=1e-4)
global_env = Environment(load_path=load_path, starting_index=index, final_index=index+1)
global_net = A3C_Network()
agent = Agent(0, global_net.n_inputs_policy, global_net.n_inputs_matching,
global_net.n_actions_policy, trainer, load_path, model_path)
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
coord = tf.train.Coordinator()
if load_model:
ckpt = tf.train.get_checkpoint_state(model_path)
saver.restore(sess, ckpt.model_checkpoint_path)
sess.run(global_env.index.assign(index))
sess.run(global_env.final_index.assign(index+1))
else:
sess.run(tf.global_variables_initializer())
agent_test = lambda: agent.test(sess, coord)
t = threading.Thread(target=(agent_test))
t.start()
t.join()
def main():
parser = argparse.ArgumentParser(description='LUBAN runner')
register_model_args(parser)
params, unparsed = parser.parse_known_args(sys.argv)
sess = tf.Session()
agent = Agent(sess, params)
agent.train(checkpoint_dir="./checkpoint",
data_dir='./data/dataset-50-3-2.hdf5')
def corrmaze(c_len):
grid = np.zeros((3,c_len+2))
for x in range(1,c_len+1):
grid[0][x] = 1
grid[2][x] = 1
agents = [Agent("[255,0]",(0,0),(0,0),(c_len+1,0)),Agent("[0,255]",(c_len+1,2),(c_len+1,2),(0,2))]
m = Maze(grid,agents)
return m
def test_exercise(capsys):
bond = Agent("James", "Bond")
print(bond)
ionic = Agent("Ionic", "Bond")
print(ionic)
out, err = capsys.readouterr()
assert out == "My name is Bond, James Bond\nMy name is Bond, Ionic Bond\n"
def dqn(
agent: Agent,
env,
brain_name,
n_episodes: int = 10,
eps_start: float = 1.0,
eps_end: float = 0.01,
eps_decay: float = 0.995,
):
"""Deep Q-Learning.
Params
======
n_episodes (int): maximum number of training episodes
max_t (int): maximum number of timesteps per episode
eps_start (float): starting value of epsilon, for epsilon-greedy action selection
eps_end (float): minimum value of epsilon
eps_decay (float): multiplicative factor (per episode) for decreasing epsilon
"""
scores = [] # list containing scores from each episode
scores_window = deque(maxlen=100) # last 100 scores
eps = eps_start
for i_episode in range(1, n_episodes + 1):
env_info = env.reset(train_mode=True)[brain_name]
state = env_info.vector_observations[0]
score = 0
while True:
action = agent.act(state, eps)
env_info = env.step(action)[brain_name]
next_state = env_info.vector_observations[0]
reward = env_info.rewards[0]
done = env_info.local_done[0]
agent.step(state, action, reward, next_state, done)
state = next_state
score += reward
if done:
break
scores_window.append(score) # save most recent score
scores.append(score) # save most recent score
eps = max(eps_end, eps_decay * eps) # decrease epsilon
print(
f"\rEpisode {i_episode}\tAverage Score: {np.mean(scores_window):.2f}",
end="",
)
if i_episode % 100 == 0:
print(
f"\rEpisode {i_episode}\tAverage Score: {np.mean(scores_window):.2f}"
)
if np.mean(scores_window) >= 13.0:
print(
f"\nEnvironment solved in {i_episode-100:d} episodes!\tAverage Score:"
f" {np.mean(scores_window):.2f}")
torch.save(agent.qnetwork_local.state_dict(), "checkpoint.pth")
break
return scores
def main(_):
config = get_config(FLAGS) or FLAGS
config.cnn_format = 'NHWC'
ps_hosts = FLAGS.ps_hosts.split(",")
worker_hosts = FLAGS.worker_hosts.split(",")
cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})
server = tf.train.Server(cluster,
job_name=FLAGS.job_name,
task_index=FLAGS.task_index)
if FLAGS.job_name == "ps":
server.join()
elif FLAGS.job_name == "worker":
env = GymEnvironment(config)
with tf.device(tf.train.replica_device_setter(
worker_device="/job:worker/task:%d" % FLAGS.task_index,
cluster=cluster)):
lr_op = tf.placeholder('float', None, name='learning_rate')
optimizer = tf.train.RMSPropOptimizer(
lr_op, decay=0.99, momentum=0, epsilon=0.1)
agent = Agent(config, env, optimizer, lr_op)
agent.ep_end = random.sample([0.1, 0.01, 0.5], 1)[0]
print(agent.model_dir)
# Create a "supervisor", which oversees the training process.
is_chief = (FLAGS.task_index == 0)
sv = tf.train.Supervisor(is_chief=is_chief,
logdir="./logs/" + agent.model_dir,
init_op=agent.init_op,
summary_op=None,
saver=agent.saver,
global_step=agent.step_op,
save_model_secs=600)
if FLAGS.is_train:
if is_chief:
train_or_play = agent.train_with_summary
else:
train_or_play = agent.train
else:
train_or_play = agent.play
with sv.managed_session(server.target) as sess:
agent.sess = sess
agent.update_target_q_network()
train_or_play(sv, is_chief)
# Ask for all the services to stop.
sv.stop()
def generate_data(mode, num_simulations=30):
"""
Generate the dual model data from the TEST_GRID_LIST specified above.
Args:
- mode (Str): "delay" or "pressure"; whether the data generated has more
or fewer monte carlo iterations to solve the test grids
- num_simulations (int): how many data points to generate from the model
Returns:
-
"""
agent = Agent()
start = time.time()
print("Starting {mode} data generation".format(mode=mode))
model_results = [
] # item e.g. {'model':'constrained','grid_num':23,'reward':3,'best_reward':3,'id':10}
# Generate dual model "time constrained scenario"
for i in range(num_simulations):
if mode == "pressure":
n_iters = random.randrange(
0, 50
) #choose a randome integer between 20 and 30 for MC iterations
elif mode == "delay":
n_iters = random.randrange(
120, 530
) #note these ranges were chosen by looking at the dual model performance graph
# in the dual_model_data_generation.ipynb
for ind, grid_init in TEST_GRID_LIST:
testgrid = grid.Grid(5, random=False, init_pos=grid_init)
Q, policy = agent.mc_first_visit_control(testgrid.copy(),
iters=n_iters,
nn_init=True,
cutoff=0.4)
_, _, model_reward = agent.run_final_policy(testgrid.copy(),
Q,
nn_init=True,
display=False)
individual_info = {
} #information for this particular model instantiation
individual_info['id'] = i
individual_info['model'] = mode
individual_info['grid_num'] = ind
individual_info['reward'] = model_reward
individual_info['best_reward'] = grid_init['best_reward']
model_results.append(individual_info)
print("Simulation {num} took {time} seconds".format(num=i,
time=time.time() -
start))
start = time.time()
return model_results
def ddpg(agent: Agent, env, brain_name, n_agents, n_episodes: int = 10):
scores_window = deque(maxlen=100)
scores_mean_agent = []
scores_mean = []
for i_episode in range(1, n_episodes + 1):
env_info = env.reset()[brain_name]
states = env_info.vector_observations
scores = np.zeros(n_agents)
while True:
actions = agent.act(states)
env_info = env.step(actions)[brain_name]
next_states = env_info.vector_observations # get the next state
rewards = env_info.rewards
dones = env_info.local_done
agent.step(states, actions, rewards, next_states, dones)
states = next_states
scores += rewards
if np.any(dones):
break
score = np.mean(scores)
scores_window.append(score)
scores_mean_agent.append(score)
scores_mean.append(np.mean(scores_window))
print(
f"\rEpisode {i_episode}\tAverage Score: {np.mean(scores_window):.2f}",
end="",
)
if i_episode % 100 == 0:
print(
f"\rEpisode {i_episode}\tAverage Score: {np.mean(scores_window):.2f}"
)
if np.mean(scores_window) >= 30.0:
print(
f"\nEnvironment solved in {i_episode-100:d} episodes!\tAverage Score:"
f" {np.mean(scores_window):.2f}")
torch.save(agent.qnetwork_local.state_dict(), "checkpoint.pth")
break
if np.mean(scores_window) >= 30.0:
print(
"\nEnvironment solved in {:d} episodes!\tAverage Score: {:.2f}"
.format(i_episode, np.mean(scores_window)))
torch.save(agent.policy_network_local.state_dict(),
"checkpoint_policy.pth")
torch.save(agent.qnetwork_local.state_dict(),
"checkpoint_qnetwork.pth")
print("saved networks")
break
return scores_mean_agent, scores_mean
def initialize_agents_from_files(self, agent_directory):
from src.agent import Agent
agent_files = os.listdir(agent_directory)
for each_agent_file in agent_files:
if '.xml' in each_agent_file:
agent = Agent()
agent_filename = agent_directory + each_agent_file
agent.get_parameters_from_file(agent_filename, self)
self.agents.append(agent)
agent.initialize_total_assets()
def graph_dual_model_performance():
test_grids = TEST_GRIDS
all_test_list = [(key, grid) for key, grid in test_grids.items()]
sorted(all_test_list, key=lambda x: x[0])
agent = Agent()
iters = ITERS
total_normal_grid_score, total_grid1_score, total_grid2_score, total_grid3_score, total_grid4_score = [],[],[],[],[]
repeats = REPEATS
for n in iters:
print("Running iteration {n}".format(n=n))
normal_grid_score, grid1_score, grid2_score, grid3_score, grid4_score = [],[],[],[],[]
for ind, grid_init in all_test_list:
normalized_score = 0
for j in range(repeats):
grid_num = int(ind) #ind initially is a string.
best_reward = grid_init['best_reward']
testgrid = Grid(5, random=False, init_pos=grid_init)
Q, policy = agent.mc_first_visit_control(testgrid.copy(),
iters=n,
nn_init=True)
_, _, dual_model_reward = agent.run_final_policy(
testgrid.copy(), Q, nn_init=True, display=False)
normalized_score += dual_model_reward - best_reward
if grid_num < 100:
normal_grid_score.append(normalized_score / repeats)
elif grid_num < 200: #grid type 1
grid1_score.append(normalized_score / repeats)
elif grid_num < 300: #grid type 2
grid2_score.append(normalized_score / repeats)
elif grid_num < 400: #grid type 3
grid3_score.append(normalized_score / repeats)
else: #grid type 4
grid4_score.append(normalized_score / repeats)
total_normal_grid_score.append(np.mean(normal_grid_score))
total_grid1_score.append(np.mean(grid1_score))
total_grid2_score.append(np.mean(grid2_score))
total_grid3_score.append(np.mean(grid3_score))
total_grid4_score.append(np.mean(grid4_score))
# plt.plot(iters, total_normal_grid_score, label="normal grids", color="red")
plt.plot(iters, total_grid1_score, label='push dilemma', color="blue")
plt.plot(iters, total_grid2_score, label='switch dilemma', color="green")
plt.plot(iters, total_grid3_score, label='switch save', color="orange")
plt.plot(iters, total_grid4_score, label='push get', color="brown")
plt.legend()
plt.xlabel("Number of MC Iterations")
plt.ylabel("Normalized Score")
plt.title("Dual model performance on all test grids")
plt.show()
def from_image(file):
img = Image.open(file)
arr = np.array(img)
height, width, _ = arr.shape
grid = np.zeros((height, width), dtype=np.uint8)
for y in range(height):
for x in range(width):
if list(arr[y][x]) == [0, 0, 0, 255]:
grid[y][x] = 1
agents = AgentPool()
for y in range(height):
for x in range(width):
if arr[y][x][2] == 16:
name = Maze.name_from_pixel(arr[y][x])
agents.add(Agent(name))
agents.get(name).set_start((x, y))
for y in range(height):
for x in range(width):
if arr[y][x][2] == 80:
name = Maze.name_from_pixel(arr[y][x])
agents.get(name).add_waypoint((x, y))
if arr[y][x][2] == 128:
name = Maze.name_from_pixel(arr[y][x])
agents.get(name).goal = (x, y)
return Maze(grid, agents)
def main(test):
# init the environment
env = UnityEnvironment(file_name=REACHER_APP)
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
env_info = env.reset(train_mode=True)[brain_name]
# number of actions
action_size = brain.vector_action_space_size
# dimenison of the state space
state_size = env_info.vector_observations.shape[1]
# number of agents
n_agents = len(env_info.agents)
# create a DDPG agent
agent = Agent(state_size=state_size,
action_size=action_size,
n_agents=n_agents,
random_seed=1)
if not test:
# train the agent
scores = run_agent(env, agent, n_episodes=300)
_ = plot_scores(agent, scores)
else:
# test the trained agent
# load the weights from file
agent.actor_local.load_state_dict(
torch.load(f'weights/{str(agent)}_checkpoint_actor.pth'))
agent.critic_local.load_state_dict(
torch.load(f'weights/{str(agent)}_checkpoint_critic.pth'))
test_agent(env, agent, n_agents)
env.close()
def train(self, log_file_dir = "./tensorboard", index = "0"):
self.__print_upperbound()
self.__init_tensorboard(log_file_dir)
starttime = time.time()
total_data_time = 0
total_training_time = 0
for i in range(int(self.train_config['steps'])):
step_start = time.time()
X, w, y, setw = self.next_batch()
finish_data = time.time()
total_data_time += (finish_data - step_start)
self._agent.train_step(X, w, y, setw=setw)
total_training_time += time.time() - finish_data
if i % 1000 == 0 and log_file_dir:
logging.info("average time for data accessing is %s"%(total_data_time/1000))
logging.info("average time for training is %s"%(total_training_time/1000))
total_training_time = 0
total_data_time = 0
self.log_between_steps(i)
if self.save_path:
best_agent = Agent(self.config, restore_dir=self.save_path)
self._agent = best_agent
pv_vector, loss, output = self._evaluate("test")
pv = self._agent.portfolio_value
log_mean = self._agent.log_mean
logging.warning('the portfolio value train No.%s is %s log_mean is %s,'
' the training time is %d seconds' % (index, pv, log_mean, time.time() - starttime))
def test_agent():
state_space_dim = 3
action_space_dim = 4
train = Train()
agent = Agent(state_space_dim=state_space_dim,
action_space_dim=action_space_dim,
low_action=-1,
high_action=1,
load=False)
state = np.random.rand((state_space_dim))[None]
next_state = np.random.rand((state_space_dim))[None]
action = agent.get_action(state)
reward = np.array([1])
done = np.array([0])
Q_loss, policy_loss = train(agent, state, next_state, action, reward, done)
assert (True)
def initialize_agents_from_files(self, agent_directory, network_config):
from src.agent import Agent
agent_files = os.listdir(agent_directory)
self.network = nx.read_gexf(network_config)
# print(self.network.edges(data=True))
for each_agent_file in agent_files:
if '.xml' in each_agent_file:
agent = Agent()
agent_filename = agent_directory + each_agent_file
agent.get_parameters_from_file(agent_filename, self)
self.agents.append(agent)
def initialize_agents_from_files(self, agent_directory, network_config):
from src.agent import Agent
agent_files = os.listdir(agent_directory)
self.network = nx.read_gexf(network_config)
# print(self.network.edges(data=True))
for each_agent_file in agent_files:
if '.xml' in each_agent_file:
agent = Agent()
agent_filename = agent_directory + each_agent_file
agent.get_parameters_from_file(agent_filename, self)
self.agents.append(agent)
def learn_policy(self,
episodes=200,
experience_replay_samples=32,
gaussian_noise_variance=1,
exponential_average_factor=0.01,
noise_bound=None,
buffer_size=math.inf
):
pbar = tqdm(total=episodes)
gaussian_noise = self.gaussian_distribution(gaussian_noise_variance)
policy = DeterministicPolicy(
self.a_model,
additive_noise_distribution=gaussian_noise
)
buffer = ReplayBuffer(buffer_size)
reward_observer = RewardObserver()
agent = Agent(self.environment, policy)
agent.attach_observer(reward_observer)
current_episode = 0
while current_episode < episodes:
# collect transition
state, action, reward, state_next, done = agent.step()
# add to buffer
buffer.add_transition(state, action, reward, state_next, done)
# if enough transitions collected perform experience replay algorithm
if buffer.size() >= experience_replay_samples:
self.experience_replay(
buffer.sample_transitions(experience_replay_samples),
exponential_average_factor,
noise_bound=noise_bound,
noise_distribution=gaussian_noise
)
# if episode ended, update progress
if done:
current_episode += 1
pbar.update(1)
pbar.close()
return DeterministicPolicy(self.a_model), reward_observer.get_rewards()
def test_train():
train_num_episodes = APPROX_EPISODES_PER_SECOND * DESIRED_TRAIN_NUM_SECONDS
# run a quick session of training and make plots.
env = Environment()
agent = Agent(env)
agent.train(env,
num_episodes=train_num_episodes,
plot_training_rewards=False)
# assert the trained agent has different Q values to a freshly instantiated one.
fresh_env = Environment()
fresh_agent = Agent(fresh_env)
assert not np.array_equal(
fresh_agent._Q,
agent._Q
)
def test_act_tau_0(self):
config = {
'ALPHA': 0.8,
'CPUCT': 1,
'EPSILON': 0.2,
'ACTION_SIZE': 32 * 4 * 7,
'MCTS_SIMULATIONS': 3
}
action_encoder = ActionEncoder(DirectionResolver())
agent = Agent(model=None, action_encoder=action_encoder, state_encoder=StateEncoder(), name='player1', config=config)
game_root = Game()
root_node = Node(game_root)
child1 = Node(game_root.move(game_root.get_possible_moves()[0]))
edge1 = Edge(root_node, child1, 0.33, 8)
edge1.stats['N'] = 10
edge1.stats['Q'] = 0.2
root_node.edges.append(edge1)
child2 = Node(game_root.move(game_root.get_possible_moves()[1]))
edge2 = Edge(root_node, child2, 0.5, 104)
edge2.stats['N'] = 20
edge2.stats['Q'] = 0.5
root_node.edges.append(edge2)
child3 = Node(game_root.move(game_root.get_possible_moves()[2]))
edge3 = Edge(root_node, child3, 0.17, 9)
edge3.stats['N'] = 15
edge3.stats['Q'] = 0.3
root_node.edges.append(edge3)
agent.prepare_mcts_for_next_action = MagicMock()
mcts = MagicMock()
mcts.root = root_node
mcts.evaluate_leaf.return_value = 0.7
agent.mcts = mcts
mcts.move_to_leaf.return_value = (root_node, 0.5, False, [])
action, pi, value = agent.act(game_root, tau=0)
self.assertEqual(action, [9, 14])
self.assertEqual(value, 0.5)
self.assertEqual(pi[8], 10/(10 + 20 + 15))
self.assertEqual(pi[9], 15/(10 + 20 + 15))
self.assertEqual(pi[8 + 3*32], 20/(10 + 20 + 15))
def init_agent(state_size, action_size, num_agents):
global agent
print("\nInitializing agent....")
agent = Agent(state_size=state_size,
action_size=action_size,
num_agents=num_agents,
random_seed=RANDOM_SEED)
def learn_policy(self,
episodes=200,
experience_replay_samples=32,
exponential_average_factor=0.01,
entropy_coefficient=0,
buffer_size=math.inf,
updates_per_replay=1):
pbar = tqdm(total=episodes)
policy = StochasticPolicy(self.a_distribution_model)
buffer = ReplayBuffer(buffer_size)
reward_observer = RewardObserver()
agent = Agent(self.environment, policy)
agent.attach_observer(reward_observer)
current_episode = 0
while current_episode < episodes:
# collect transition
state, action, reward, state_next, done = agent.step()
# add to buffer
buffer.add_transition(state, action, reward, state_next, done)
# if enough transitions collected perform experience replay algorithm
if buffer.size() >= experience_replay_samples:
for _ in range(updates_per_replay):
self.experience_replay(
buffer.sample_transitions(experience_replay_samples),
exponential_average_factor, entropy_coefficient)
# if episode ended, update progress
if done:
current_episode += 1
if current_episode % 20 == 0:
reward_observer.plot()
reward_observer.plot_moving_average(5)
pbar.update(1)
pbar.close()
return MeanOfStochasticModel(
self.a_distribution_model), reward_observer.get_rewards()
def get_parameters_from_file(self, args):
import os
from src.environment import Environment
from src.agent import Agent
#
# Initialize Environment and give arguments
#
# we need to give environment config
environment_directory = str(args[0])
identifier = args[1]
# calling the Environment Class
environment = Environment(environment_directory, identifier)
# # get the agent_directory from the environment
agent_directory = environment.agent_directory
# # and loop over all agents in the directorys
listings = os.listdir(agent_directory)
for file in listings:
if 'agent5.xml' in file:
# #
# # TESTING
# #
# # test whether the parameters are read properly
text = "Initiating agent object..\n"
self.print_info(text)
agent = Agent()
environment.agents.append(agent)
print agent
text = "Reading in parameters by calling method..\n"
self.print_info(text)
agent_filename = agent_directory + file
agent.get_parameters_from_file(agent_filename, environment)
print agent
def __init__(self):
self.world = World(*SimulationConfig.word_size)
self.graphic = Graphic(self.world, *SimulationConfig.pane_size)
if SimulationConfig.fixed_sick_cases:
for i in range(SimulationConfig.population_size):
if i < SimulationConfig.fixed_cases_count:
self.world.add_agent_on_free(Agent(self.world, True))
else:
self.world.add_agent_on_free(Agent(self.world, False))
else:
for i in range(SimulationConfig.population_size):
self.world.add_agent_on_free(
Agent(
self.world,
get_it_with_probability(
SimulationConfig.create_sick_agent_probability,
True, False)))
self.statistic = Statistic(self.world)
def read_agent(version):
nn = Residual_CNN(config['REG_CONST'], config['LEARNING_RATE'], (2, 4, 8),
config['ACTION_SIZE'], config['HIDDEN_CNN_LAYERS'],
config['MOMENTUM'])
m_tmp = nn.read(version)
nn.model.set_weights(m_tmp.get_weights())
player = Agent(nn,
ActionEncoder(DirectionResolver()),
StateEncoder(),
name='player' + str(version),
config=config)
return player
def play(ctx, steps, noise):
env, state_space_dim, action_space_dim, state_norm_array, min_action, \
max_action = setup_env()
# noise_process = OUNoise(
# dim=action_space_dim,
# sigma=SIGMA,
# theta=THETA,
# dt=1e-2)
# noise_process = NormalNoise(
# dim=action_space_dim,
# sigma=SIGMA)
# noise_process = LinearSegmentNoise(
# dim=action_space_dim,
# sigma=SIGMA)
noise_process = SmoothNoiseND(steps=steps,
dim=action_space_dim,
sigma=SIGMA)
agent = Agent(state_space_dim,
action_space_dim,
layer_dims=LAYERS_DIMS,
low_action=min_action,
high_action=max_action,
noise_process=noise_process,
load=True)
state = env.reset()
agent.actor.summary()
agent.critic.summary()
for i in range(steps):
action = agent.get_action(state[None], with_exploration=noise)[0]
state, reward, done, _ = env \
.step(action)
state = state
env.render()
def main():
window_size = 5
episode_count = 10
stock_name = "^GSPC_2011"
agent = Agent(window_size)
market = Market(window_size=window_size, stock_name=stock_name)
batch_size = 32
start_time = time.time()
for e in range(episode_count + 1):
print("Episodio" + str(e) + "/" + str(episode_count))
agent.reset()
state, price_data = market.reset() # ToDo: get the initial state
for t in range(market.last_data_index):
# obtener acción actual del agente
# llamar al método act() del agente considerando el estado actual
action, bought_price = agent.act(state, price_data)
# obtener siguiente estado del agente según el mercado
next_state, next_price_data, reward, done =\
market.get_next_state_reward(action, bought_price)
# añadir trasacción a la memoria
agent.memory.append((state, action, reward, next_state, done))
# aprender de la historia solo en el caso que haya memoria
if len(agent.memory) > batch_size:
agent.experience_replay(batch_size)
state = next_state
price_data = next_price_data
if done:
print("--------------------------------")
print("Ganancias totales: {0}".format(
agent.get_total_profit()))
print("--------------------------------")
if e % 10 == 0:
if not os.path.exists("models"):
os.mkdir("models")
agent.model.save("models/model_rl" + str(e))
end_time = time.time()
training_time = round(end_time - start_time)
print("Entrenamiento tomó {0} segundos.".format(training_time))
def main(_):
config = get_config(FLAGS) or FLAGS
config.cnn_format = 'NHWC'
ps_hosts = FLAGS.ps_hosts.split(",")
worker_hosts = FLAGS.worker_hosts.split(",")
cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})
server = tf.train.Server(cluster,
job_name=FLAGS.job_name,
task_index=FLAGS.task_index)
if FLAGS.job_name == "ps":
server.join()
elif FLAGS.job_name == "worker":
env = GymEnvironment(config)
with tf.device(
tf.train.replica_device_setter(
worker_device="/job:worker/task:%d" % FLAGS.task_index,
cluster=cluster)):
lr_op = tf.placeholder('float', None, name='learning_rate')
optimizer = tf.train.RMSPropOptimizer(lr_op,
decay=0.99,
momentum=0,
epsilon=0.1)
agent = Agent(config, env, optimizer, lr_op)
agent.ep_end = random.sample([0.1, 0.01, 0.5], 1)[0]
print(agent.model_dir)
# Create a "supervisor", which oversees the training process.
is_chief = (FLAGS.task_index == 0)
sv = tf.train.Supervisor(is_chief=is_chief,
logdir="./logs/" + agent.model_dir,
init_op=agent.init_op,
summary_op=None,
saver=agent.saver,
global_step=agent.step_op,
save_model_secs=600)
if FLAGS.is_train:
if is_chief:
train_or_play = agent.train_with_summary
else:
train_or_play = agent.train
else:
train_or_play = agent.play
with sv.managed_session(server.target) as sess:
agent.sess = sess
agent.update_target_q_network()
train_or_play(sv, is_chief)
# Ask for all the services to stop.
sv.stop()
def walker():
environment = gym.make("Pendulum-v0")
print(environment.action_space)
print(environment.observation_space)
class action_distribution_model(nn.Module):
def __init__(self):
super(action_distribution_model, self).__init__()
self.secquential = nn.Sequential(nn.Linear(3, 24), nn.ReLU(),
nn.Linear(24, 1, bias=False),
nn.Tanh())
def forward(self, x):
mean = self.secquential(x)
mean = mean * 4
return MultivariateNormal(mean, torch.eye(1) * 0.25)
distribution = action_distribution_model()
optimizer = torch.optim.Adam(distribution.parameters(), lr=0.01)
v_model = nn.Sequential(nn.Linear(3, 24), nn.ReLU(), nn.Linear(24, 1))
v_optimizer = torch.optim.Adam(v_model.parameters(), lr=0.01)
learner = PPO(environment, distribution, optimizer, discount_factor=0.99)
opt_policy, history = learner.learn_policy(epochs=250,
actor_iterations=10,
episodes_per_update=2)
plt.plot(history)
plt.xlabel('episode')
plt.ylabel('total reward')
plt.savefig("score.png")
a = Agent(environment, opt_policy)
while input("continue ") == "c":
a.perform_episode(render=True)
def initialize_agents_from_files(self, agent_directory):
from src.agent import Agent
agent_files = os.listdir(agent_directory)
self.network = nx.Graph()
for each_agent_file in agent_files:
if '.xml' in each_agent_file:
agent = Agent()
agent_filename = agent_directory + each_agent_file
agent.get_nodes_for_graph(agent_filename, self)
for each_agent_file in agent_files:
if '.xml' in each_agent_file:
agent = Agent()
agent_filename = agent_directory + each_agent_file
agent.get_parameters_from_file(agent_filename, self)
self.agents.append(agent)