GAIL¶
The Generative Adversarial Imitation Learning (GAIL) uses expert trajectories to recover a cost function and then learn a policy.
Learning a cost function from expert demonstrations is called Inverse Reinforcement Learning (IRL). The connection between GAIL and Generative Adversarial Networks (GANs) is that it uses a discriminator that tries to seperate expert trajectory from trajectories of the learned policy, which has the role of the generator here.
Notes¶
 Original paper: https://arxiv.org/abs/1606.03476
Warning
Images are not yet handled properly by the current implementation
If you want to train an imitation learning agent¶
Step 1: Generate expert data¶
You can either train a RL algorithm in a classic setting, use another controller (e.g. a PID controller) or human demonstrations.
We recommend you to take a look at pretraining section
or directly look at stable_baselines/gail/dataset/
folder to learn more about the expected format for the dataset.
Here is an example of training a Soft ActorCritic model to generate expert trajectories for GAIL:
from stable_baselines import SAC
from stable_baselines.gail import generate_expert_traj
# Generate expert trajectories (train expert)
model = SAC('MlpPolicy', 'Pendulumv0', verbose=1)
# Train for 60000 timesteps and record 10 trajectories
# all the data will be saved in 'expert_pendulum.npz' file
generate_expert_traj(model, 'expert_pendulum', n_timesteps=60000, n_episodes=10)
Step 2: Run GAIL¶
In case you want to run Behavior Cloning (BC)
Use the .pretrain()
method (cf guide).
Others
Thanks to the open source:
 @openai/imitation
 @carpedm20/deeprltensorflow
Can I use?¶
 Recurrent policies: ❌
 Multi processing: ✔️ (using MPI)
 Gym spaces:
Space  Action  Observation 

Discrete  ✔️  ✔️ 
Box  ✔️  ✔️ 
MultiDiscrete  ❌  ✔️ 
MultiBinary  ❌  ✔️ 
Example¶
import gym
from stable_baselines import GAIL, SAC
from stable_baselines.gail import ExpertDataset, generate_expert_traj
# Generate expert trajectories (train expert)
model = SAC('MlpPolicy', 'Pendulumv0', verbose=1)
generate_expert_traj(model, 'expert_pendulum', n_timesteps=100, n_episodes=10)
# Load the expert dataset
dataset = ExpertDataset(expert_path='expert_pendulum.npz', traj_limitation=10, verbose=1)
model = GAIL("MlpPolicy", 'Pendulumv0', dataset, verbose=1)
# Note: in practice, you need to train for 1M steps to have a working policy
model.learn(total_timesteps=1000)
model.save("gail_pendulum")
del model # remove to demonstrate saving and loading
model = GAIL.load("gail_pendulum")
env = gym.make('Pendulumv0')
obs = env.reset()
while True:
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
Parameters¶

class
stable_baselines.gail.
GAIL
(policy, env, expert_dataset=None, hidden_size_adversary=100, adversary_entcoeff=0.001, g_step=3, d_step=1, d_stepsize=0.0003, verbose=0, _init_setup_model=True, **kwargs)[source]¶ Generative Adversarial Imitation Learning (GAIL)
Warning
Images are not yet handled properly by the current implementation
Parameters:  policy – (ActorCriticPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, CnnLstmPolicy, …)
 env – (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
 expert_dataset – (ExpertDataset) the dataset manager
 gamma – (float) the discount value
 timesteps_per_batch – (int) the number of timesteps to run per batch (horizon)
 max_kl – (float) the kullback leiber loss threashold
 cg_iters – (int) the number of iterations for the conjugate gradient calculation
 lam – (float) GAE factor
 entcoeff – (float) the weight for the entropy loss
 cg_damping – (float) the compute gradient dampening factor
 vf_stepsize – (float) the value function stepsize
 vf_iters – (int) the value function’s number iterations for learning
 hidden_size – ([int]) the hidden dimension for the MLP
 g_step – (int) number of steps to train policy in each epoch
 d_step – (int) number of steps to train discriminator in each epoch
 d_stepsize – (float) the reward giver stepsize
 verbose – (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
 _init_setup_model – (bool) Whether or not to build the network at the creation of the instance
 full_tensorboard_log – (bool) enable additional logging when using tensorboard WARNING: this logging can take a lot of space quickly

action_probability
(observation, state=None, mask=None, actions=None)[source]¶ If
actions
isNone
, then get the model’s action probability distribution from a given observation depending on the action space the output is:
 Discrete: probability for each possible action
 Box: mean and standard deviation of the action output
However if
actions
is notNone
, this function will return the probability that the given actions are taken with the given parameters (observation, state, …) on this model.Warning
When working with continuous probability distribution (e.g. Gaussian distribution for continuous action) the probability of taking a particular action is exactly zero. See http://blog.christianperone.com/2019/01/ for a good explanation
Parameters:  observation – (np.ndarray) the input observation
 state – (np.ndarray) The last states (can be None, used in recurrent policies)
 mask – (np.ndarray) The last masks (can be None, used in recurrent policies)
 actions – (np.ndarray) (OPTIONAL) For calculating the likelihood that the given actions are chosen by the model for each of the given parameters. Must have the same number of actions and observations. (set to None to return the complete action probability distribution)
Returns: (np.ndarray) the model’s action probability

get_env
()¶ returns the current environment (can be None if not defined)
Returns: (Gym Environment) The current environment

learn
(total_timesteps, callback=None, seed=None, log_interval=100, tb_log_name='GAIL', reset_num_timesteps=True)[source]¶ Return a trained model.
Parameters:  total_timesteps – (int) The total number of samples to train on
 seed – (int) The initial seed for training, if None: keep current seed
 callback – (function (dict, dict)) > boolean function called at every steps with state of the algorithm. It takes the local and global variables. If it returns False, training is aborted.
 log_interval – (int) The number of timesteps before logging.
 tb_log_name – (str) the name of the run for tensorboard log
 reset_num_timesteps – (bool) whether or not to reset the current timestep number (used in logging)
Returns: (BaseRLModel) the trained model

classmethod
load
(load_path, env=None, **kwargs)[source]¶ Load the model from file
Parameters:  load_path – (str or filelike) the saved parameter location
 env – (Gym Envrionment) the new environment to run the loaded model on (can be None if you only need prediction from a trained model)
 kwargs – extra arguments to change the model when loading

predict
(observation, state=None, mask=None, deterministic=False)[source]¶ Get the model’s action from an observation
Parameters:  observation – (np.ndarray) the input observation
 state – (np.ndarray) The last states (can be None, used in recurrent policies)
 mask – (np.ndarray) The last masks (can be None, used in recurrent policies)
 deterministic – (bool) Whether or not to return deterministic actions.
Returns: (np.ndarray, np.ndarray) the model’s action and the next state (used in recurrent policies)

pretrain
(dataset, n_epochs=10, learning_rate=0.0001, adam_epsilon=1e08, val_interval=None)[source]¶ Pretrain a model using behavior cloning: supervised learning given an expert dataset.
NOTE: only Box and Discrete spaces are supported for now.
Parameters:  dataset – (ExpertDataset) Dataset manager
 n_epochs – (int) Number of iterations on the training set
 learning_rate – (float) Learning rate
 adam_epsilon – (float) the epsilon value for the adam optimizer
 val_interval – (int) Report training and validation losses every n epochs. By default, every 10th of the maximum number of epochs.
Returns: (BaseRLModel) the pretrained model

save
(save_path)[source]¶ Save the current parameters to file
Parameters: save_path – (str or filelike object) the save location