def _init(args):
env_name = args.env_name
print('Using environment %s' % env_name)
params_dict = {
'env_name': [env_name],
'rundir': [args.rundir],
'ent_wt': [args.trpo_ent],
'trpo_step': [args.trpo_step],
'hid_size': [args.hid_size],
'hid_layers': [args.hid_layers],
'many_runs': [args.repeat > 1]
}
if args.repeat > 1:
# stacked parallel thing doesn't work, bleh
warnings.warn(
"You're trying to use --repeat N for N > 1, but that "
"disables parallel sampling. This is probably going to be "
"heinously slow or something, use at own risk.")
# parallel_sampler.initialize(n_parallel=1)
# parallel_sampler.set_seed(1)
run_sweep_parallel(main, params_dict, repeat=args.repeat)
else:
parallel_sampler.initialize(n_parallel=8)
parallel_sampler.set_seed(1)
run_sweep_serial(main, params_dict, repeat=1)
expert_trajs=experts,
state_only=True,
fusion=fusion,
max_itrs=10)
policy = GaussianMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
algo = IRLTRPO(
env=env,
policy=policy,
irl_model=irl_model,
n_itr=1000,
batch_size=10000,
max_path_length=500,
discount=0.99,
store_paths=True,
irl_model_wt=1.0,
entropy_weight=0.1,
zero_environment_reward=True,
baseline=LinearFeatureBaseline(env_spec=env.spec),
)
with rllab_logdir(algo=algo, dirname='data/ant_state_irl/%s' % exp_name):
with tf.Session():
algo.train()
if __name__ == "__main__":
params_dict = {'fusion': [True]}
run_sweep_parallel(main, params_dict, repeat=3)
default=1e-5,
type=float,
help='step size for --adaptive-beta',
)
if __name__ == "__main__":
args = parser.parse_args()
env_name = args.env_name
print('Args:', args)
params_dict = {
'method': [args.method],
'rundir': [args.rundir],
'env_name': [env_name],
'disc_step': [args.disc_step],
'trpo_step': [args.trpo_step],
'ent_wt': [args.trpo_ent],
'hid_size': [args.hid_size],
'hid_layers': [args.hid_layers],
'disc_iters': [args.disc_iters],
'disc_batch_size': [args.disc_batch_size],
'disc_gp': [args.dreg_gp],
'max_traj': [args.max_traj],
# VAIR/VAIL params
'beta': [getattr(args, 'beta', None)],
'adaptive_beta': [getattr(args, 'adaptive_beta', None)],
'target_kl': [getattr(args, 'adaptive_beta_target_kl', None)],
'beta_step': [getattr(args, 'adaptive_beta_step', None)],
}
run_sweep_parallel(main, params_dict, repeat=args.repeat)
# run_sweep_serial(main, params_dict, repeat=1)
