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)