def main():
env = TfEnv(CustomGymEnv('PointMazeLeft-v0'))
experts = load_latest_experts('data/point', n=50)
irl_model = GCLDiscrim(env_spec=env.spec, expert_trajs=experts)
policy = GaussianMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
algo = IRLTRPO(
env=env,
policy=policy,
irl_model=irl_model,
n_itr=200,
batch_size=2000,
max_path_length=100,
discount=0.99,
store_paths=True,
discrim_train_itrs=50,
irl_model_wt=1.0,
entropy_weight=0.1, # this should be 1.0 but 0.1 seems to work better
zero_environment_reward=True,
baseline=LinearFeatureBaseline(env_spec=env.spec))
with rllab_logdir(algo=algo, dirname='data/point_gcl'):
with tf.Session() as sess:
algo.train()
test_pointmaze(sess.run(policy))
def main(num_examples=50, discount=0.99):
env = TfEnv(GymEnv('Pendulum-v0', record_video=False, record_log=False))
experts = load_latest_experts('data/pendulum', n=num_examples)
irl_model = GCLDiscrim(env_spec=env.spec, expert_trajs=experts)
policy = GaussianMLPPolicy(name='policy', env_spec=env.spec, hidden_sizes=(32, 32))
algo = IRLTRPO(
env=env,
policy=policy,
irl_model=irl_model,
n_itr=200,
batch_size=2000,
max_path_length=100,
discount=discount,
store_paths=True,
discrim_train_itrs=50,
irl_model_wt=1.0,
entropy_weight=0.1, # this should be 1.0 but 0.1 seems to work better
zero_environment_reward=True,
baseline=LinearFeatureBaseline(env_spec=env.spec)
)
with rllab_logdir(algo=algo, dirname='data/pendulum_gcl'):
with tf.Session():
algo.train()
def main():
env = TfEnv(GymEnv('Ant-v1', record_video=False, record_log=False))
experts = load_latest_experts('data/ant', n=50)
irl_model = GCLDiscrim(
env_spec=env.spec,
expert_trajs=experts,
discrim_arch=disentangled_net)
policy = GaussianMLPPolicy(name='policy', env_spec=env.spec, hidden_sizes=(32, 32))
algo = IRLTRPO(
env=env,
policy=policy,
irl_model=irl_model,
n_itr=2000,
batch_size=10000,
max_path_length=1000,
discount=0.995,
store_paths=True,
discrim_train_itrs=50,
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_airl'):
with tf.Session():
algo.train()