def main():
env = TfEnv(GymEnv('Pendulum-v0', record_video=False, record_log=False))
experts = load_latest_experts('data/pendulum', n=5)
irl_model = GAIL(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=1000,
max_path_length=100,
discount=0.99,
store_paths=True,
discrim_train_itrs=50,
irl_model_wt=1.0,
entropy_weight=0.0, # GAIL should not use entropy unless for exploration
zero_environment_reward=True,
baseline=LinearFeatureBaseline(env_spec=env.spec))
with rllab_logdir(algo=algo, dirname='data/pendulum_gail'):
with tf.Session():
algo.train()
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 = GCLDiscrimTrajectory(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_traj'):
with tf.Session():
algo.train()
def main():
env = TfEnv(CustomGymEnv('PointMazeLeft-v0'))
experts = load_latest_experts('data/point', n=50)
irl_model = GCLDiscrimTrajectory(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=2000,
batch_size=10000,
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_traj'):
with tf.Session():
algo.train()
test_pointmaze(sess.run(policy))
def main(eval_reward = False):
env = TfEnv(GymEnv('Pendulum-v0', record_video=False, record_log=False))
n_experts = 10
experts = load_latest_experts('plotting/pendulum_final', n=n_experts)
dirname='data/pendulum' # dir to save logs and images
irl_model = AIRLStateAction(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=1000,
batch_size=1000,
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),
eval_reward=True,
fig_dir = dirname
)
# with rllab_logdir(algo=algo, dirname='data/pendulum_gcl{}'.format(n_experts)):
with rllab_logdir(algo=algo, dirname=dirname):
with tf.Session():
algo.fig_dirname = dirname
algo.train()
def main(exp_name=None, fusion=False, visible_gpus='0', discount=0.99):
env = TfEnv(GymEnv('Swimmer-v3', record_video=False, record_log=False))
gpu_options = tf.GPUOptions(allow_growth=True,visible_device_list=args.visible_gpus)
tf_config = tf.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1, gpu_options=gpu_options)
experts = load_latest_experts('data/swimmer', n=5, visible_gpus=visible_gpus)
irl_model = AIRL(discount=discount, env=env, expert_trajs=experts, state_only=False, fusion=args.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=1000,
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/swimmer_airl_state_action'):
with tf.Session(config=tf_config) as sess:
algo.train(sess)
def main():
env = TfEnv(GymEnv('Pendulum-v0', record_video=False, record_log=False))
experts = load_latest_experts('data/pendulum', n=5)
irl_model = GAIL(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=1000,
max_path_length=100,
discount=0.99,
store_paths=True,
discrim_train_itrs=50,
irl_model_wt=1.0,
entropy_weight=0.0, # GAIL should not use entropy unless for exploration
zero_environment_reward=True,
baseline=LinearFeatureBaseline(env_spec=env.spec)
)
with rllab_logdir(algo=algo, dirname='data/pendulum_gail'):
with tf.Session():
algo.train()
def main():
env = TfEnv(
GymEnv('HRI_AirSim_Landing-v0', record_video=False, record_log=False))
experts = load_latest_experts('data/airsim', n=5)
irl_model = AIRLStateAction(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=10,
batch_size=100,
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/airsim_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()
def main(exp_name=None, fusion=False, visible_gpus='0', discount=0.99, debug=False, n_val=1, n_rew=1, \
max_nstep=1, exp_folder=None, state_only=False, score_discrim=True, score_method=None):
env = TfEnv(GymEnv('HalfCheetah-v3', record_video=False, record_log=False))
gpu_options = tf.GPUOptions(allow_growth=True,
visible_device_list=visible_gpus)
tf_config = tf.ConfigProto(inter_op_parallelism_threads=1,
intra_op_parallelism_threads=1,
gpu_options=gpu_options)
# load ~5 iterations worth of data from each forward RL experiment as demos
experts = load_latest_experts('data/half_cheetah',
n=5,
visible_gpus=visible_gpus)
sess = tf.Session(config=tf_config)
# sess = tf_debug.LocalCLIDebugWrapperSession(sess)
max_path_length = 500
irl_model = AIRL_Bootstrap(discount=discount, env=env, expert_trajs=experts, \
state_only=state_only, fusion=fusion, max_itrs=10, \
score_discrim=score_discrim, debug = debug, \
max_nstep = max_nstep, n_value_funct = n_val, \
n_rew_funct = n_rew, score_method=score_method)
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=max_path_length,
discount=discount,
store_paths=True,
irl_model_wt=1.0,
entropy_weight=0.1,
zero_environment_reward=True,
baseline=LinearFeatureBaseline(env_spec=env.spec),
discrim_train_itrs=50)
# temp_folder = '/media/data/temp_exp_nstep/maze_right_state_bootstrap_%d_irl/%s'
dirname = 'data/half_cheetah_bootstrap_%d_irl/%s/%s' % (
max_nstep, exp_folder, exp_name
) if exp_folder is not None else 'data/half_cheetah_bootstrap_%d_irl/%s' % (
max_nstep, exp_name)
with rllab_logdir(algo=algo, dirname=dirname):
sess.__enter__()
algo.train(sess)
sess.close()
