def main(): parser = base_config.get_base_config() parser = cem_config.get_cem_config(parser) args = base_config.make_parser(parser) if args.write_log: if args.output_dir == None: args.output_dir = "log" else: pass log_path = str(args.output_dir)+'/pets-cem-' + str(args.task) + '/seed-' + str(args.seed) +'/num_planning_traj-' + str(args.num_planning_traj) +'/plannging depth-' + str(args.planning_depth) +'/timesteps_per_batch-' + str(args.timesteps_per_batch) +'/random_timesteps-' + str(args.random_timesteps) +'/max_timesteps-' + str(args.max_timesteps)+'/' logger.set_file_handler(path=log_path, prefix='', time_str="0") print('Training starts at {}'.format(init_path.get_abs_base_dir())) from mbbl.trainer import shooting_trainer from mbbl.sampler import singletask_pets_sampler from mbbl.worker import cem_worker from mbbl.network.policy.random_policy import policy_network if args.gt_dynamics: from mbbl.network.dynamics.groundtruth_forward_dynamics import \ dynamics_network else: from mbbl.network.dynamics.deterministic_forward_dynamics import \ dynamics_network if args.gt_reward: from mbbl.network.reward.groundtruth_reward import reward_network else: from mbbl.network.reward.deterministic_reward import reward_network train(shooting_trainer, singletask_pets_sampler, cem_worker, dynamics_network, policy_network, reward_network, args)
def main(): parser = base_config.get_base_config() parser = rs_config.get_rs_config(parser) args = base_config.make_parser(parser) if args.write_log: logger.set_file_handler(path=args.output_dir, prefix='mbrl-rs' + args.task, time_str=args.exp_id) print('Training starts at {}'.format(init_path.get_abs_base_dir())) from mbbl.trainer import shooting_trainer from mbbl.sampler import singletask_sampler from mbbl.worker import rs_worker from mbbl.network.policy.random_policy import policy_network if args.gt_dynamics: from mbbl.network.dynamics.groundtruth_forward_dynamics import \ dynamics_network else: from mbbl.network.dynamics.deterministic_forward_dynamics import \ dynamics_network if args.gt_reward: from mbbl.network.reward.groundtruth_reward import reward_network else: from mbbl.network.reward.deterministic_reward import reward_network train(shooting_trainer, singletask_sampler, rs_worker, dynamics_network, policy_network, reward_network, args)
def main(): parser = base_config.get_base_config() parser = ilqr_config.get_ilqr_config(parser) args = base_config.make_parser(parser) if args.write_log: logger.set_file_handler(path=args.output_dir, prefix='mbrl-ilqr' + args.task, time_str=args.exp_id) print('Training starts at {}'.format(init_path.get_abs_base_dir())) from mbbl.trainer import shooting_trainer from mbbl.sampler import singletask_ilqr_sampler from mbbl.worker import model_worker from mbbl.network.policy.random_policy import policy_network if args.gt_dynamics: from mbbl.network.dynamics.groundtruth_forward_dynamics import \ dynamics_network else: from mbbl.network.dynamics.deterministic_forward_dynamics import \ dynamics_network if args.gt_reward: from mbbl.network.reward.groundtruth_reward import reward_network else: from mbbl.network.reward.deterministic_reward import reward_network if (not args.gt_reward) or not (args.gt_dynamics): raise NotImplementedError('Havent finished! Oooooops') train(shooting_trainer, singletask_ilqr_sampler, model_worker, dynamics_network, policy_network, reward_network, args)