def define_jobs_context(self, context):
boot_root = self.get_boot_root()
data_central = self.get_data_central()
GlobalConfig.global_load_dir('default')
recipe_agentlearn_by_parallel_concurrent(context, data_central,
Exp23.explogs_learn, n=8, only_agents=['exp23_diffeof', 'exp23_diffeo_fast'])
recipe_agentlearn_by_parallel(context, data_central,
Exp23.explogs_learn,
only_agents=['stats2'])
from diffeo2dds_learn.programs.devel.save_video import video_visualize_diffeo_stream1_robot
for c, id_robot in iterate_context_names(context, Exp23.robots):
out = os.path.join(context.get_output_dir(),
'videos', '%s-diffeo_stream1.mp4' % id_robot)
c.comp_config(video_visualize_diffeo_stream1_robot,
id_robot=id_robot, boot_root=boot_root,
out=out)
for id_robot in Exp23.robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
jobs_publish_learning_agents_robots(context, boot_root,
Exp23.agents, Exp23.robots)
def jobs_learn_real(context, data_central, real_robots, explogs_learn):
for id_robot in real_robots:
recipe_agentlearn_by_parallel(context, data_central,
only_robots=[id_robot],
episodes=explogs_learn)
boot_root = data_central.get_boot_root()
for id_robot in real_robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
def define_jobs_context(self, context):
boot_config = get_boot_config()
boot_config.agents.instance(Exp20.agents[0])
boot_root = self.get_boot_root()
data_central = self.get_data_central()
recipe_episodeready_by_convert2(context, boot_root, id_robot=Exp20.id_robot)
recipe_agentlearn_by_parallel(context, data_central, Exp20.explogs_learn)
jobs_publish_learning(context, Exp20.agents, id_robot=Exp20.id_robot)
def define_jobs_context(self, context):
boot_root = self.get_boot_root()
data_central = self.get_data_central()
recipe_agentlearn_by_parallel(context, data_central, Exp21.explogs_learn)
for id_robot in Exp21.robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
jobs_publish_learning_agents_robots(context, boot_root,
Exp21.agents, Exp21.robots)
def jobs_learn_parallel(context, data_central, explogs_learn, agents, robots,
episodes_per_tranche=50):
""" Learn parallel """
boot_root = data_central.get_boot_root()
recipe_agentlearn_by_parallel(context, data_central, explogs_learn,
episodes_per_tranche=episodes_per_tranche)
recipe_agent_servo(context, create_report=True)
for id_robot in robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
jobs_publish_learning_agents_robots(context, boot_root, agents, robots)
def define_jobs_context(self, context):
boot_root = self.get_boot_root()
data_central = self.get_data_central()
GlobalConfig.global_load_dir("default")
recipe_agentlearn_by_parallel_concurrent_reps(
context, data_central, Exp27.explogs_learn, n=8, max_reps=20, only_agents=["exp23_diffeof"]
)
recipe_agentlearn_by_parallel(context, data_central, Exp27.explogs_learn, only_agents=["stats2", "cmdstats"])
for id_robot in Exp27.robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
jobs_publish_learning_agents_robots(context, boot_root, Exp27.agents, Exp27.robots)
def jobs_learn_simulations(context, data_central, simulated_robots, num_sim_episodes,
max_episode_len, explorer, episodes_per_tranche=50):
sim_episodes = [episode_id_exploration(explorer, i) for i in range(num_sim_episodes)]
for id_robot in simulated_robots:
# recipe_episodeready_by_simulation(context, data_central, id_robot,
# explorer, max_episode_len)
recipe_episodeready_by_simulation_tranches(context, data_central, id_robot,
explorer, max_episode_len,
sim_episodes, episodes_per_tranche=50)
recipe_agentlearn_by_parallel(context, data_central,
only_robots=[id_robot],
episodes=sim_episodes,
episodes_per_tranche=episodes_per_tranche)
def define_jobs_context(self, context):
boot_root = self.get_boot_root()
data_central = self.get_data_central()
GlobalConfig.global_load_dir('default')
recipe_agentlearn_by_parallel(context, data_central, Exp29.explogs_learn)
for id_robot in Exp29.robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
combinations = iterate_context_names_pair(context, Exp29.nmaps, Exp29.robots)
for c, id_episode, id_robot in combinations:
jobs_navigation_map(c,
outdir=context.get_output_dir(),
data_central=data_central,
id_robot=id_robot,
id_episode=id_episode)
def define_jobs_context(self, context):
boot_root = self.get_boot_root()
data_central = self.get_data_central()
agents = Exp31.agents
robots = Exp31.robots
explogs_learn = Exp31.explogs_learn
explogs_test = Exp31.explogs_test
recipe_agentlearn_by_parallel(context, data_central, explogs_learn)
for id_robot in robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
jobs_servo_field_agents(context, id_robot=id_robot,
agents=agents, episodes=explogs_test)
jobs_publish_learning_agents_robots(context, boot_root, agents, robots)
def define_jobs_context(self, context):
boot_root = self.get_boot_root()
data_central = self.get_data_central()
GlobalConfig.global_load_dir('default')
recipe_agentlearn_by_parallel_concurrent(context, data_central, \
Exp24.explogs_learn, n=8, only_agents=['exp23_diffeof', 'exp23_diffeo_fast'])
recipe_agentlearn_by_parallel(context, data_central,
Exp24.explogs_learn,
only_agents=['stats2'])
for id_robot in Exp24.robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
jobs_publish_learning_agents_robots(context, boot_root,
Exp24.agents, Exp24.robots)
def define_jobs_context(self, context):
boot_root = self.get_boot_root()
data_central = self.get_data_central()
# for vehicles
GlobalConfig.global_load_dir('${B11_SRC}/bvapps/bdse1')
recipe_episodeready_by_convert2(context, boot_root)
recipe_episodeready_by_simulation_tranches(context, data_central,
explorer=Exp42.explorer,
episodes=Exp42.simulated_episodes,
max_episode_len=30,
episodes_per_tranche=50)
for c in Exp42.combinations:
recipe_agentlearn_by_parallel(context, data_central, c['episodes'],
only_robots=[c['id_robot']],
intermediate_reports=False,
episodes_per_tranche=50)
jobs_publish_learning_agents_robots(context, boot_root,
Exp42.agents, Exp42.robots)
