def build_long_term_larget(env: Environment, dt, N):
ind = env.get_current_waypoint_index()
track = env.get_track()
pos = env.car.get_position()
desired_v = 80
dist_travel = desired_v * dt
def get_point(start, end, d_to_go):
x0, y0 = start
x1, y1 = end
dy = y1 - y0
dx = x1 - x0
d = np.linalg.norm((dx, dy))
x = x0 + d_to_go * dx / d
y = y0 + d_to_go * dy / d
return np.array((x, y))
cur_pos = np.array(pos)
ind = ind % len(track)
cur_target = np.array(track[ind][2:4])
result = [pos]
for i in range(N - 1):
remain_dist = np.linalg.norm(cur_target - cur_pos) - dist_travel
if remain_dist > 0:
p = get_point(cur_pos, cur_target, dist_travel)
result.append(p)
cur_pos = p
else:
# must ensure distance between 2 target points larger than dist_travel
cur_pos = cur_target
ind = (ind + 1) % len(track)
cur_target = np.array(track[ind][2:4])
p = get_point(cur_pos, cur_target, -remain_dist)
result.append(p)
cur_pos = p
return result
def initialize(self, config):
self.config = config
# initialize agent
self.agent = Agent(critic_arch=self.critic_arch,
actor_arch=self.actor_arch,
critic_hidden_size=self.config['CRITIC_SIZE'],
actor_hidden_size=self.config['ACTOR_SIZE'],
action_size=self.config['ACTION_SIZE'],
memory_size=self.config['MEMORY_SIZE'],
critic_learning_rate=self.config['CRITIC_LR'],
actor_learning_rate=self.config['ACTOR_LR'],
gamma=self.config['GAMMA'],
tau=self.config['TAU'])
# intialize environment
self.env = Environment(agent=self.agent,
classifier=self.classifier,
img_dir=self.config['IMG_PATH'],
img_size=self.config['IMG_SIZE'],
blob_size=self.config['BLOB_SIZE'],
done_threshold=self.config['DONE_THRESHOLD'])
self.noise = OUNoise(self.config['ACTION_SIZE'])
def main():
"""
:return:
"""
setup_logging()
op = OptionParser("Usage: %prog [options] " "(tasks_config.json | tasks_config.py)")
op.add_option('-o', '--output', default='results.out', help='File where the simulation results are saved.')
op.add_option('--scramble', action='store_true', default=False,
help='Randomly scramble the words in the tasks for ' 'a human player.')
op.add_option('-w', '--show-world', action='store_true', default=False,
help='shows a visualization of the world in the console ' '(mainly for debugging)')
op.add_option('-d', '--time-delay', default=0, type=float,
help='adds some delay between each timestep for easier' ' visualization.')
op.add_option('-l', '--learner', default='learners.human_learner.HumanLearner',
help='Defines the type of learner.')
op.add_option('-v', '--view', default='BaseView', help='Viewing mode.')
op.add_option('-s', '--serializer', default='core.serializer.StandardSerializer',
help='Sets the encoding of characters into bits')
op.add_option('--learner-cmd', help='The cmd to run to launch RemoteLearner.')
op.add_option('--learner-port', default=5556, help='Port on which to accept remote learner.')
op.add_option('--max-reward-per-task', default=10, type=int,
help='Maximum reward that we can give to a learner for' ' a given task.')
opt, args = op.parse_args()
if len(args) == 0:
op.error("Tasks schedule configuration file required.")
tasks_config_file = args[0] # Retrieve task configuration
logger = logging.getLogger(__name__)
logger.info("Starting new evaluation session")
serializer = create_serializer(opt.serializer) # Set hoe enviroment produces and interprets a bit signal
learner = create_learner(opt.learner, serializer, opt.learner_cmd, opt.learner_port) # Create learner
task_scheduler = create_tasks_from_config(tasks_config_file) # Create tasks, add to scheduler to be served
env = Environment(serializer, task_scheduler, opt.scramble, opt.max_reward_per_task) # Construct environment
session = Session(env, learner, opt.time_delay) # a learning session
view = create_view(opt.view, opt.learner, env, session, serializer, opt.show_world) # setup view
try:
learner.set_view(view) # Send interface to human learner
except AttributeError: # not human. pass
pass
try:
view.initialize() # talk
session.run()
except BaseException:
view.finalize()
save_results(session, opt.output)
raise
else:
view.finalize()
def main():
env = gym.make('CarRacing-v0')
env = Environment(env=env, FPS=50.0)
done = False
env.reset()
# car = env.unwrapped.car
# w = car.wheels[0]
dt = 1 / FPS
prev_a = MAX_a
prev_steer = 0
total_reward = 0
# ego_car = Car(car, prev_a, prev_steer)
while not done:
print('########################')
long_term_xs, long_term_ys = env.calc_long_term_targets()
a, steer, x, y = long_term_MPC(env.car,
list(zip(long_term_xs, long_term_ys)),
dt)
short_term_N = 5
short_term_target = list(zip(x, y))[:short_term_N]
a, steer, x, y = short_term_MPC(env.car, short_term_target, dt)
print(a, steer, env.car.get_velocity())
a = a / MAX_a
steer = steer
if a > 0:
action = Action(steer, a / 10, 0)
else:
action = Action(steer, 0, -a)
_, r, done, _ = env.step(action)
env.car.take_control(action)
total_reward += r
env.render()
print(total_reward)
def process_world(conn, opt, tasks_config_file, world_id):
try:
serializer = StandardSerializer()
task_scheduler = create_tasks_from_config(tasks_config_file)
env = Environment(serializer, task_scheduler, opt.scramble,
opt.max_reward_per_task, not opt.bit_mode)
learner = create_learner(opt.learner, serializer, opt.learner_cmd,
opt.learner_port, not opt.bit_mode)
session = Session(env, learner, opt.time_delay)
args = conn.recv()
while not (args is None):
episode_id, step_count, seed, weight = args
# INTERACTION BETWEEN ENVIRONMENT AND AGENT
learner.net.set_genotype_weight(weight, seed)
del weight
episode_reward = session.iterate_n(step_count)
# save_results(session, opt.output)
conn.send((episode_reward, seed))
args = conn.recv()
except BaseException as e:
print(e)
conn.send(None)
__author__ = 'aldnav'
from core.environment import Environment
if __name__ == '__main__':
env = Environment()
env.simulate()
def main():
setup_logging()
op = OptionParser("Usage: %prog [options] "
"(tasks_config.json | tasks_config.py)")
op.add_option('-o',
'--output',
default='results.out',
help='File where the simulation results are saved.')
op.add_option('--scramble',
action='store_true',
default=False,
help='Randomly scramble the words in the tasks for '
'a human player.')
op.add_option('-w',
'--show-world',
action='store_true',
default=False,
help='shows a visualization of the world in the console '
'(mainly for debugging)')
op.add_option('-d',
'--time-delay',
default=0,
type=float,
help='adds some delay between each timestep for easier'
' visualization.')
op.add_option('-l',
'--learner',
default='learners.human_learner.HumanLearner',
help='Defines the type of learner.')
op.add_option('-v', '--view', default='BaseView', help='Viewing mode.')
op.add_option('--learner-cmd',
help='The cmd to run to launch RemoteLearner.')
op.add_option('--learner-port',
default=5556,
type=int,
help='Port on which to accept remote learner.')
op.add_option('--learner-address',
help='Network address on which the remote learner listens.')
op.add_option('--max-reward-per-task',
default=2147483647,
type=int,
help='Maximum reward that we can give to a learner for'
' a given task.')
op.add_option('--curses',
action='store_true',
default=False,
help='Uses standard output instead of curses library.')
op.add_option('--bit-mode',
action='store_true',
default=False,
help='Environment receives input in bytes.')
opt, args = op.parse_args()
if len(args) == 0:
op.error("Tasks schedule configuration file required.")
# retrieve the task configuration file
tasks_config_file = args[0]
logger = logging.getLogger(__name__)
logger.info("Starting new evaluation session")
# we choose how the environment will produce and interpret
# the bit signal
serializer = StandardSerializer()
# create a learner (the human learner takes the serializer)
learner = create_learner(opt.learner, serializer, opt.learner_cmd,
opt.learner_port, opt.learner_address,
not opt.bit_mode)
# create our tasks and put them into a scheduler to serve them
task_scheduler = create_tasks_from_config(tasks_config_file)
# construct an environment
env = Environment(serializer, task_scheduler, opt.scramble,
opt.max_reward_per_task, not opt.bit_mode)
# a learning session
session = Session(env, learner, opt.time_delay)
# setup view
view = create_view(opt.view, opt.learner, env, session, serializer,
opt.show_world, opt.curses, not opt.bit_mode)
try:
# send the interface to the human learner
learner.set_view(view)
except AttributeError:
# this was not a human learner, nothing to do
pass
try:
view.initialize()
# ok guys, talk
session.run()
except BaseException:
view.finalize()
save_results(session, opt.output)
raise
else:
view.finalize()
class Trainer:
def __init__(self, config, critic_arch, actor_arch, classifier):
self.config = config
self.critic_arch, self.actor_arch, self.classifier = critic_arch, actor_arch, classifier
self.initialize(self.config)
def initialize(self, config):
self.config = config
# initialize agent
self.agent = Agent(critic_arch=self.critic_arch,
actor_arch=self.actor_arch,
critic_hidden_size=self.config['CRITIC_SIZE'],
actor_hidden_size=self.config['ACTOR_SIZE'],
action_size=self.config['ACTION_SIZE'],
memory_size=self.config['MEMORY_SIZE'],
critic_learning_rate=self.config['CRITIC_LR'],
actor_learning_rate=self.config['ACTOR_LR'],
gamma=self.config['GAMMA'],
tau=self.config['TAU'])
# intialize environment
self.env = Environment(agent=self.agent,
classifier=self.classifier,
img_dir=self.config['IMG_PATH'],
img_size=self.config['IMG_SIZE'],
blob_size=self.config['BLOB_SIZE'],
done_threshold=self.config['DONE_THRESHOLD'])
self.noise = OUNoise(self.config['ACTION_SIZE'])
def run(self):
batch_size = self.config['BATCH_SIZE']
rewards = []
avg_rewards = []
for episode in range(self.config['N_EPISODES']):
state = self.env.reset()
self.noise.reset()
episode_reward = 0
bar = progress_bar(range(self.config['MAX_LENGTH_EPISODE']))
for step in bar:
action = self.agent.get_action(state)
action = self.noise.get_action(action=action, t=step)
new_state, reward, done, info = self.env.step(action)
self.agent.memory.push(state, action, reward, new_state, done)
if len(self.agent.memory) > batch_size:
self.agent.update(batch_size)
state = new_state
episode_reward += reward
bar.comment = f"reward: {reward:.3f} - episode_reward: {episode_reward:.3f}"
if done:
print(
f"Done episode {episode:<3} with reward of: {episode_reward:.2f}, avg reward: {np.mean(rewards)}"
)
break
rewards.append(episode_reward)
avg_rewards.append(np.mean(rewards))
return rewards, avg_rewards
def save(self, name=None):
"""
specify `name` to override experiment name in config
"""
name = name or self.config['NAME']
state_dict = {
'critic': self.agent.critic.state_dict(),
'actor': self.agent.actor.state_dict(),
'critic_target': self.agent.critic_target.state_dict(),
'actor_target': self.agent.actor_target.state_dict()
}
torch.save(state_dict, f"{self.config['WEIGHT_PATH']}/{name}.pth")
def load(self, name=None):
"""
specify `name` to override experiment name in config
"""
name = name or self.config['NAME']
state_dict = torch.load(f"{self.config['WEIGHT_PATH']}/{name}.pth")
self.agent.critic.load_state_dict(state_dict['critic'])
self.agent.actor.load_state_dict(state_dict['actor'])
self.agent.critic_target.load_state_dict(state_dict['critic_target'])
self.agent.actor_target.load_state_dict(state_dict['actor_target'])
def create_base_environment(self):
env = Environment()
# env.add_primitives(self.MATH_BINDINGS)
env.add_primitives(self.OP_BINDINGS)
env.add_primitives({'exit': (self.exit_function(), 'Void -> Void')})
return env