def main(exp_name=None, fusion=False):
env = TfEnv(
CustomGymEnv('CustomAnt-v0', record_video=False, record_log=False))
# load ~2 iterations worth of data from each forward RL experiment as demos
experts = load_latest_experts_multiple_runs('data/ant_data_collect', n=2)
irl_model = AIRL(env=env,
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()
def main(exp_name=None, fusion=False, visible_gpus='0',discount=0.99):
env = TfEnv(CustomGymEnv('CustomAnt-v0', 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 ~2 iterations worth of data from each forward RL experiment as demos
experts = load_latest_experts_multiple_runs('data/ant_data_collect', n=2, visible_gpus=visible_gpus)
irl_model = AIRL(discount=discount, env=env, 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=discount,
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(config=tf_config) as sess:
algo.train(sess)
def main(exp_name=None, fusion=True):
# env = TfEnv(CustomGymEnv('CustomAnt-v0', record_video=False, record_log=False))
env = TfEnv(
CustomGymEnv('CustomAnt-v0', record_video=False, record_log=True))
# load ~2 iterations worth of data from each forward RL experiment as demos
experts = load_latest_experts_multiple_runs('data/ant_data_collect', n=2)
#experts = load_latest_experts('data/ant_data_collect', n=5)
#qvar: inverse model q(a|s,s')
qvar = GaussianMLPInversePolicy(name='qvar_model',
env_spec=env.spec,
hidden_sizes=(32, 32))
qvar_model = Qvar(env=env,
qvar=qvar,
expert_trajs=experts,
fusion=True,
max_itrs=10)
#Empowerment-based Adversarial Inverse Reinforcement Learning, set score_discrim=True
irl_model = EAIRL(env=env,
expert_trajs=experts,
state_only=False,
fusion=fusion,
max_itrs=10,
score_discrim=True)
#Empowerment-based potential functions gamma* Phi(s')-Phi(s)
empw_model = Empowerment(env=env, fusion=True, max_itrs=4)
t_empw_model = Empowerment(env=env,
scope='t_efn',
fusion=True,
max_itrs=2,
name='empowerment2')
policy = GaussianMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
algo = IRLTRPO(
env=env,
policy=policy,
empw=empw_model,
tempw=t_empw_model,
qvar_model=qvar_model,
irl_model=irl_model,
n_itr=3000, #130,
batch_size=20000,
max_path_length=500,
discount=0.99,
store_paths=True,
target_empw_update=5,
irl_model_wt=1.0,
entropy_weight=0.1,
lambda_i=1.0,
zero_environment_reward=True,
baseline=LinearFeatureBaseline(env_spec=env.spec),
plot=False)
with rllab_logdir(algo=algo, dirname='data/ant_state_irl'):
#with rllab_logdir(algo=algo, dirname='data/ant_state_irl/%s' % exp_name): # if you use multiple runs, use this line instead of above
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(
CustomGymEnv('PointMazeRight-v0', 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 ~2 iterations worth of data from each forward RL experiment as demos
experts = load_latest_experts_multiple_runs('data/maze_right_data_collect',
n=2,
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),
)
# temp_folder = '/media/data/temp_exp_nstep/maze_right_state_bootstrap_%d_irl/%s'
dirname = 'data/maze_right_state_bootstrap_%d_irl/%s/%s' % (
max_nstep, exp_folder, exp_name
) if exp_folder is not None else 'data/maze_right_state_bootstrap_%d_irl/%s' % (
max_nstep, exp_name)
with rllab_logdir(algo=algo, dirname=dirname):
sess.__enter__()
algo.train(sess)
sess.close()
def main(exp_name=None, params_folder='data/ant_state_irl'):
# env = TfEnv(CustomGymEnv('PointMazeLeft-v0', record_video=True, record_log=True,force_reset=True))
env = TfEnv(
CustomGymEnv('DisabledAnt-v0',
record_video=False,
record_log=False,
force_reset=False))
irl_itr = 90 # earlier IRL iterations overfit less; either 80 or 90 seems to work well. But I usually search through 60,65,70,75, .. uptil 100
#params_file = os.path.join(DATA_DIR, '%s/itr_%d.pkl' % (params_folder, irl_itr))
params_file = os.path.join(DATA_DIR, 'itr_%d.pkl' % (irl_itr))
prior_params = load_prior_params(params_file)
'''q_itr = 400 # earlier IRL iterations overfit less; 100 seems to work well.
#params_file = os.path.join(DATA_DIR, '%s/itr_%d.pkl' % (params_folder, irl_itr))
params_file = os.path.join(DATA_DIR, 'itr_%d.pkl' % (q_itr))
prior_params_q = load_prior_params(params_file)'''
experts = load_latest_experts_multiple_runs('data/ant_data_collect', n=2)
qvar = GaussianMLPInversePolicy(name='qvar_model',
env_spec=env.spec,
hidden_sizes=(32, 32))
qvar_model = Qvar(env=env, qvar=qvar, expert_trajs=None, max_itrs=10)
irl_model = EAIRL(env=env,
expert_trajs=experts,
state_only=False,
score_discrim=False)
empw_model = Empowerment(env=env, max_itrs=1)
t_empw_model = Empowerment(env=env,
scope='t_efn',
max_itrs=2,
name='empowerment2')
policy = GaussianMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
algo = IRLTRPO(
init_irl_params=prior_params['irl_params'],
init_empw_params=None, #prior_params['empw_params'],
init_qvar_params=None, #prior_params['qvar_params'],
init_policy_params=prior_params['policy_params'], #None
env=env,
policy=policy,
empw=empw_model,
tempw=t_empw_model,
qvar_model=qvar_model,
irl_model=irl_model,
n_itr=2000,
batch_size=20000,
max_path_length=500,
discount=0.99,
store_paths=False,
train_irl=True,
train_empw=True,
train_qvar=True,
irl_model_wt=1.0,
entropy_weight=0.1,
zero_environment_reward=True,
baseline=LinearFeatureBaseline(env_spec=env.spec),
log_params_folder=params_folder,
log_experiment_name=exp_name,
# plot=True,
)
with rllab_logdir(algo=algo, dirname='data/ant_transfer'): #%s'%exp_name):
#with rllab_logdir(algo=algo, dirname='data/ant_transfer%s'%exp_name):
with tf.Session():
algo.train()
def main(exp_name=None, fusion=False, latent_dim=3):
max_path_length = 100
info_coeff = 0.1
imitation_coeff = 0.01
batch_size = 16
meta_batch_size = 50
max_itrs = 20
pre_epoch = 1000
entropy_weight = 1.0
reward_arch = relu_net
if reward_arch == relu_net:
layers = 2
d_hidden = 32
reward_arch_args = {
'layers': layers,
'd_hidden': d_hidden,
}
else:
layers, d_hidden = 0, 0
reward_arch_args = None
tf.reset_default_graph()
env = TfEnv(
CustomGymEnv('PointMazeLeft-v0', record_video=False, record_log=False))
# load ~2 iterations worth of data from each forward RL experiment as demos
experts = load_latest_experts_multiple_runs(
'data/maze_left_data_collect_discrete-15', n=4, latent_dim=latent_dim)
# contexual policy pi(a|s,m)
policy = GaussianMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
# approximate posterior q(m|tau)
context_encoder_spec = EnvSpec(
observation_space=Box(
np.tile(
np.concatenate((env.observation_space.low[:-latent_dim],
env.action_space.low)), max_path_length),
np.tile(
np.concatenate((env.observation_space.high[:-latent_dim],
env.action_space.high)), max_path_length)),
action_space=Box(np.zeros(latent_dim), np.ones(latent_dim)),
)
context_encoder = GaussianMLPPolicy(name='context_encoder',
env_spec=context_encoder_spec,
hidden_sizes=(128, 128))
pretrain_model = Pretrain(experts,
policy,
context_encoder,
env,
latent_dim,
batch_size=400,
kl_weight=0.1,
epoch=pre_epoch)
# pretrain_model = None
if pretrain_model is None:
pre_epoch = 0
irl_model = InfoAIRL(env=env,
policy=policy,
context_encoder=context_encoder,
reward_arch=reward_arch,
reward_arch_args=reward_arch_args,
expert_trajs=experts,
state_only=True,
max_path_length=max_path_length,
fusion=fusion,
max_itrs=max_itrs,
meta_batch_size=meta_batch_size,
imitation_coeff=imitation_coeff,
info_coeff=info_coeff,
latent_dim=latent_dim)
algo = MetaIRLTRPO(
env=env,
policy=policy,
irl_model=irl_model,
randomize_policy=True,
pretrain_model=pretrain_model,
n_itr=3000,
meta_batch_size=meta_batch_size,
batch_size=batch_size,
max_path_length=max_path_length,
discount=0.99,
store_paths=True,
train_irl=True,
irl_model_wt=1.0,
entropy_weight=entropy_weight,
zero_environment_reward=True,
baseline=LinearFeatureBaseline(env_spec=env.spec),
)
if fusion:
dirname = 'data_fusion_discrete_new/maze_wall_meta_irl_imitcoeff-%s_infocoeff-%s_mbs-%s_bs-%s_itr-%s_preepoch-%s_entropy-%s_RandomPol_Rew-%s-%s/%s' % (
imitation_coeff, info_coeff, meta_batch_size, batch_size, max_itrs,
pre_epoch, entropy_weight, layers, d_hidden, exp_name)
else:
dirname = 'data_discrete_new/maze_wall_meta_irl_imitcoeff-%s_infocoeff-%s_mbs-%s_bs-%s_itr-%s_preepoch-%s_entropy-%s_RandomPol_Rew-%s-%s/%s' % (
imitation_coeff, info_coeff, meta_batch_size, batch_size, max_itrs,
pre_epoch, entropy_weight, layers, d_hidden, exp_name)
with rllab_logdir(algo=algo, dirname=dirname):
with tf.Session():
algo.train()
def main(exp_name=None, latent_dim=3, params_folder=None):
max_path_length = 100
batch_size = 16
meta_batch_size = 1
reward_arch = relu_net
if reward_arch == relu_net:
layers = 2
d_hidden = 32
reward_arch_args = {
'layers': layers,
'd_hidden': d_hidden,
}
else:
layers, d_hidden = 0, 0
reward_arch_args = None
# tf.reset_default_graph()
env = TfEnv(
CustomGymEnv('PointMazeRight-v0', record_video=False,
record_log=False))
barrier_range = [0.2, 0.6]
barrier_y = 0.3
# load ~2 iterations worth of data from each forward RL experiment as demos
experts = load_latest_experts_multiple_runs(
'/atlas/u/lantaoyu/projects/InfoAIRL/data/maze_left_data_collect',
n=4,
latent_dim=latent_dim)
irl_itr_list = [2800]
for irl_itr in irl_itr_list:
# params_file = os.path.join(DATA_DIR, '%s/itr_%d.pkl' % (params_folder, irl_itr))
params_file = os.path.join(DATA_DIR, 'itr_%d.pkl' % irl_itr)
prior_params = load_prior_params(params_file)
init_context_encoder_params = load_prior_params(
params_file, 'context_params')
# params_file = os.path.join(DATA_DIR, 'itr_%d.pkl' % (irl_itr-800))
policy_prior_params = load_prior_params(params_file, 'policy_params')
# policy_prior_params = None
# contexual policy pi(a|s,m)
policy = GaussianMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
# approximate posterior q(m|tau)
context_encoder_spec = EnvSpec(
observation_space=Box(
np.tile(
np.concatenate((env.observation_space.low[:-latent_dim],
env.action_space.low)), max_path_length),
np.tile(
np.concatenate((env.observation_space.high[:-latent_dim],
env.action_space.high)), max_path_length)),
action_space=Box(np.zeros(latent_dim), np.ones(latent_dim)),
)
context_encoder = GaussianMLPPolicy(name='context_encoder',
env_spec=context_encoder_spec,
hidden_sizes=(128, 128))
irl_model = InfoAIRL(env=env,
expert_trajs=experts,
reward_arch=reward_arch,
reward_arch_args=reward_arch_args,
context_encoder=context_encoder,
state_only=True,
max_path_length=max_path_length,
meta_batch_size=meta_batch_size,
latent_dim=latent_dim)
savedir = 'data_fusion_discrete/visualize_reward_right-%s' % irl_itr
if not os.path.isdir(savedir):
os.mkdir(savedir)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
irl_model.context_encoder.set_param_values(
init_context_encoder_params)
policy.set_param_values(policy_prior_params)
irl_model.set_params(prior_params)
boundary_low = -0.1
boundary_high = 0.6
expert_obs, expert_acts, expert_contexts = irl_model.extract_paths(
irl_model.expert_trajs,
keys=('observations', 'actions', 'contexts'),
T=max_path_length)
expert_trajs = np.concatenate(
(expert_obs, expert_acts),
axis=-1) # num_experts x T x (state_dim + act_dim)
grid_size = 0.005
rescale = 1. / grid_size
for itr in range(100):
expert_traj_batch, m_batch = irl_model.sample_batch(
expert_trajs,
expert_contexts,
batch_size=1,
warm_up=False,
warm_up_idx=False)
obs_batch = []
num_y = 0
for pos_y in np.arange(boundary_low, boundary_high, grid_size):
num_y += 1
num_x = 0
for pos_x in np.arange(boundary_low, boundary_high,
grid_size):
num_x += 1
obs_batch.append([pos_x, pos_y, 0.])
obs_batch = np.array(obs_batch).reshape(
[1, -1, max_path_length, 3])
expert_traj_batch = np.tile(
np.reshape(expert_traj_batch, [1, 1, max_path_length, -1]),
[1, obs_batch.shape[1], 1, 1])
reward = tf.get_default_session().run(
irl_model.reward,
feed_dict={
irl_model.expert_traj_var: expert_traj_batch,
irl_model.obs_t: obs_batch
})
score = reward[:, 0]
ax = sns.heatmap(score.reshape([num_x, num_y]),
cmap="YlGnBu_r")
ax.scatter((m_batch[0][0][0] - boundary_low) * rescale,
(m_batch[0][0][1] - boundary_low) * rescale,
marker='*',
s=150,
c='r',
edgecolors='k',
linewidths=0.5)
ax.scatter((0.3 - boundary_low +
np.random.uniform(low=-0.05, high=0.05)) * rescale,
(0. - boundary_low +
np.random.uniform(low=-0.05, high=0.05)) * rescale,
marker='o',
s=120,
c='white',
linewidths=0.5,
edgecolors='k')
ax.plot([(barrier_range[0] - boundary_low) * rescale,
(barrier_range[1] - boundary_low) * rescale],
[(barrier_y - boundary_low) * rescale,
(barrier_y - boundary_low) * rescale],
color='k',
linewidth=10)
ax.invert_yaxis()
plt.axis('off')
plt.savefig(savedir + '/%s.png' % itr)
print('Save Itr', itr)
plt.close()
from inverse_rl.utils.log_utils import load_latest_experts_multiple_runs
import pdb
experts = load_latest_experts_multiple_runs('data/ant_data_collect', n=2)
def main(exp_name=None, latent_dim=3, params_folder=None):
max_path_length = 100
batch_size = 32
meta_batch_size = 50
entropy_weight = 0.1
left = 'right'
if_filtered = True
# tf.reset_default_graph()
if left == 'left':
env = TfEnv(
CustomGymEnv('PointMazeLeft-v0',
record_video=False,
record_log=False))
else:
env = TfEnv(
CustomGymEnv('PointMazeRight-v0',
record_video=False,
record_log=False))
# load ~2 iterations worth of data from each forward RL experiment as demos
experts = load_latest_experts_multiple_runs(
'/atlas/u/lantaoyu/projects/InfoAIRL/data/maze_left_data_collect',
n=4,
latent_dim=latent_dim)
if if_filtered:
experts_filtered = []
good_range = [0.1, 0.4] #[0.3, 0.5]
for expert in experts:
if expert['contexts'][0,
0] >= good_range[0] and expert['contexts'][
0, 0] <= good_range[1]:
experts_filtered.append(expert)
assert len(experts_filtered) >= meta_batch_size
experts_filtered = experts_filtered[:-(len(experts_filtered) %
meta_batch_size)]
experts = experts_filtered
irl_itr_list = [2800]
results = []
for irl_itr in irl_itr_list:
params_file = os.path.join(DATA_DIR, 'itr_%d.pkl' % irl_itr)
prior_params = load_prior_params(params_file)
init_context_encoder_params = load_prior_params(
params_file, 'context_params')
policy_prior_params = None
# contexual policy pi(a|s,m)
policy = GaussianMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
# approximate posterior q(m|tau)
context_encoder_spec = EnvSpec(
observation_space=Box(
np.tile(
np.concatenate((env.observation_space.low[:-latent_dim],
env.action_space.low)), max_path_length),
np.tile(
np.concatenate((env.observation_space.high[:-latent_dim],
env.action_space.high)), max_path_length)),
action_space=Box(np.zeros(latent_dim), np.ones(latent_dim)),
)
context_encoder = GaussianMLPPolicy(name='context_encoder',
env_spec=context_encoder_spec,
hidden_sizes=(128, 128))
irl_model = InfoAIRL(env=env,
expert_trajs=experts,
context_encoder=context_encoder,
state_only=True,
max_path_length=max_path_length,
meta_batch_size=meta_batch_size,
latent_dim=latent_dim)
algo = MetaIRLTRPO(
init_irl_params=prior_params,
init_pol_params=policy_prior_params, #policy_prior_params,
init_context_encoder_params=init_context_encoder_params,
env=env,
policy=policy,
irl_model=irl_model,
n_itr=150,
meta_batch_size=meta_batch_size,
batch_size=batch_size,
max_path_length=max_path_length,
discount=0.99,
store_paths=True,
train_irl=True, # True
train_context_only=True,
train_policy=True,
irl_model_wt=1.0,
entropy_weight=entropy_weight,
zero_environment_reward=True,
baseline=LinearFeatureBaseline(env_spec=env.spec),
log_params_folder=params_folder,
log_experiment_name=exp_name,
)
with rllab_logdir(
algo=algo,
dirname=
'data_finetune/maze_finetune_discrete-entropy-%s-irl_itr-%s-%s-%s-generalize/%s'
% (entropy_weight, irl_itr, left,
'filter' if if_filtered else '', exp_name)):
with tf.Session():
algo.train()
results.append((irl_itr, np.max(algo.pol_ret)))
tf.reset_default_graph()
print(results)
