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()
# 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)
