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)