GAIL¶
Generative Adversarial Imitation Learning (GAIL)
Notes¶
 Original paper: https://arxiv.org/abs/1606.03476
If you want to train an imitation learning agent¶
Step 1: Download expert data¶
Download the expert data into ./data
, download link
Step 2: Run GAIL¶
Run with single thread:
python m stable_baselines.gail.run_mujoco
Run with multiple threads:
mpirun np 16 python m stable_baselines.gail.run_mujoco
See help (h
) for more options.
In case you want to run Behavior Cloning (BC)
python m stable_baselines.gail.behavior_clone
See help (h
) for more options.
OpenAI Maintainers:
 YuanHong Liao, andrewliao11_at_gmail_dot_com
 Ryan Julian, ryanjulian_at_gmail_dot_com
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  ❌  ✔️ 
Parameters¶

class
stable_baselines.gail.
GAIL
(policy, env, pretrained_weight=False, hidden_size_adversary=100, adversary_entcoeff=0.001, expert_dataset=None, save_per_iter=1, checkpoint_dir='/tmp/gail/ckpt/', g_step=1, d_step=1, task_name='task_name', d_stepsize=0.0003, verbose=0, _init_setup_model=True, **kwargs)[source]¶ Generative Adversarial Imitation Learning (GAIL)
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)
 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
 pretrained_weight – (str) the save location for the pretrained weights
 hidden_size – ([int]) the hidden dimension for the MLP
 expert_dataset – (Dset) the dataset manager
 save_per_iter – (int) the number of iterations before saving
 checkpoint_dir – (str) the location for saving checkpoints
 g_step – (int) number of steps to train policy in each epoch
 d_step – (int) number of steps to train discriminator in each epoch
 task_name – (str) the name of the task (can be None)
 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

action_probability
(observation, state=None, mask=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')[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
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)

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