def main(argv):
args = get_args(argv)
x = env_args(args)
print(x)
policy_mod = import_module(args.policy_source)
policy_class = getattr(policy_mod, 'DaggerPolicy')
policy = policy_class(**vars(args))
env = make(**vars(x))
dagger = Dagger(env, policy, **vars(args))
dagger.learn()
env.close()
def main(argv):
parser = argparse.ArgumentParser(description='block_world')
parser.add_argument('--greedy', dest='greedy', action='store_true')
parser.add_argument('--no-greedy', dest='greedy', action='store_false')
parser.add_argument('--dims', type=int, default=3)
parser.add_argument('--span', type=int, default=10)
parser.add_argument('--max-timesteps', type=int, default=2000000)
parser.add_argument('--exploration_fraction', type=float, default=0.1)
parser.add_argument('--exploration_final_eps', type=float, default=0.01)
parser.add_argument('--l2-penalty', type=float, default=None)
parser.add_argument('--continous-actions',
dest='continous_actions',
action='store_true')
parser.add_argument('--no-continous-actions',
dest='continous_actions',
action='store_false')
parser.add_argument('--reach-minimum', type=float, default=0.1)
parser.set_defaults(greedy=False)
parser.set_defaults(continous_actions=False)
cmd_args = parser.parse_args(argv)
print(cmd_args)
env = make(span=cmd_args.span,
dims=cmd_args.dims,
greedy=cmd_args.greedy,
l2_penalty=cmd_args.l2_penalty,
continous_actions=cmd_args.continous_actions,
reach_minimum=cmd_args.reach_minimum)
dagger = Dagger(env,
DaggerPolicy,
num_rollouts=25,
train_batch_size=25,
train_epochs=20,
iterations=20,
dir_name='tmp_storage')
dagger.learn(save_file_name="dagger_dist_world")
env.close()
def learn(env, policy):
dagger = Dagger(env)
dagger.learn(policy)