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 = mf_config.get_mf_config(parser)
parser = il_config.get_il_config(parser)
args = base_config.make_parser(parser)
if args.write_log:
logger.set_file_handler(path=args.output_dir,
prefix='gail-mfrl-mf-' + args.task,
time_str=args.exp_id)
# no random policy for model-free rl
assert args.random_timesteps == 0
print('Training starts at {}'.format(init_path.get_abs_base_dir()))
from mbbl.trainer import gail_trainer
from mbbl.sampler import singletask_sampler
from mbbl.worker import mf_worker
import mbbl.network.policy.trpo_policy
import mbbl.network.policy.ppo_policy
policy_network = {
'ppo': mbbl.network.policy.ppo_policy.policy_network,
'trpo': mbbl.network.policy.trpo_policy.policy_network
}[args.trust_region_method]
# here the dynamics and reward are simply placeholders, which cannot be
# called to pred next state or reward
from mbbl.network.dynamics.base_dynamics import base_dynamics_network
from mbbl.network.reward.GAN_reward import reward_network
train(gail_trainer, singletask_sampler, mf_worker, base_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)
def main():
parser = base_config.get_base_config()
parser = rs_config.get_rs_config(parser)
parser = il_config.get_il_config(parser)
args = base_config.make_parser(parser)
args = il_config.post_process_config(args)
if args.write_log:
args.log_path = logger.set_file_handler(path=args.output_dir,
prefix='inverse_dynamics' +
args.task,
time_str=args.exp_id)
print('Training starts at {}'.format(init_path.get_abs_base_dir()))
train(args)
def main():
parser = base_config.get_base_config()
parser = metrpo_config.get_metrpo_config(parser)
args = base_config.make_parser(parser)
if args.write_log:
logger.set_file_handler(path=args.output_dir,
prefix='mbrl-metrpo-' + args.task,
time_str=args.exp_id)
print('Training starts at {}'.format(init_path.get_abs_base_dir()))
from mbbl.trainer import metrpo_trainer
from mbbl.sampler import singletask_metrpo_sampler
from mbbl.worker import metrpo_worker
from mbbl.network.dynamics.deterministic_forward_dynamics import dynamics_network
from mbbl.network.policy.trpo_policy import policy_network
from mbbl.network.reward.groundtruth_reward import reward_network
train(metrpo_trainer, singletask_metrpo_sampler, metrpo_worker,
dynamics_network, policy_network, reward_network, args)
def main():
parser = base_config.get_base_config()
parser = ilqr_config.get_ilqr_config(parser)
parser = gps_config.get_gps_config(parser)
args = base_config.make_parser(parser)
if args.write_log:
logger.set_file_handler(path=args.output_dir,
prefix='mbrl-gps-' + args.task,
time_str=args.exp_id)
print('Training starts at {}'.format(init_path.get_abs_base_dir()))
from mbbl.trainer import gps_trainer
from mbbl.sampler import singletask_sampler
from mbbl.worker import mf_worker
from mbbl.network.policy.gps_policy_gmm_refit import policy_network
assert not args.gt_dynamics and args.gt_reward
from mbbl.network.dynamics.linear_stochastic_forward_dynamics_gmm_prior \
import dynamics_network
from mbbl.network.reward.groundtruth_reward import reward_network
train(gps_trainer, singletask_sampler, mf_worker,
dynamics_network, policy_network, reward_network, args)
actual_reward - quadratic_pred_reward)
if abs(quadratic_pred_reward - actual_reward) > 0.1:
import pdb
pdb.set_trace()
max_error = max(max_error,
abs(quadratic_pred_reward - actual_reward))
print('max_error: ', max_error)
# import pdb; pdb.set_trace()
print('max_error: ', max_error)
elif TEST == 'DYNAMICS_DERIVATIVE':
from mbbl.config import base_config
from mbbl.config import ilqr_config
parser = base_config.get_base_config()
parser = ilqr_config.get_ilqr_config(parser)
args = base_config.make_parser(parser)
args.gt_dynamics = 1
# candidate_names = walker.env.WALKER
# candidate_names = reacher.env.ARM_2D
for env_name in candidate_names:
args.task = args.task_name = env_name
env_info = env_register.get_env_info(env_name)
# env = pendulum.env(env_name, 123, {})
env, _ = env_register.make_env(env_name, 123, {})
env_info = env_register.get_env_info(env_name)
derivative_env, _ = env_register.make_env(env_name, 123, {})
env_info = env_register.get_env_info(env_name)
derivative_env.reset()
network = groundtruth_forward_dynamics.dynamics_network(
args, None, None, env_info['ob_size'], env_info['action_size'])
network.build_network()