Source code for stable_baselines.gail.model

from stable_baselines.trpo_mpi import TRPO

[docs]class GAIL(TRPO): """ Generative Adversarial Imitation Learning (GAIL) .. warning:: Images are not yet handled properly by the current implementation :param policy: (ActorCriticPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, CnnLstmPolicy, ...) :param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str) :param expert_dataset: (ExpertDataset) the dataset manager :param gamma: (float) the discount value :param timesteps_per_batch: (int) the number of timesteps to run per batch (horizon) :param max_kl: (float) the Kullback-Leibler loss threshold :param cg_iters: (int) the number of iterations for the conjugate gradient calculation :param lam: (float) GAE factor :param entcoeff: (float) the weight for the entropy loss :param cg_damping: (float) the compute gradient dampening factor :param vf_stepsize: (float) the value function stepsize :param vf_iters: (int) the value function's number iterations for learning :param hidden_size: ([int]) the hidden dimension for the MLP :param g_step: (int) number of steps to train policy in each epoch :param d_step: (int) number of steps to train discriminator in each epoch :param d_stepsize: (float) the reward giver stepsize :param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug :param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance :param full_tensorboard_log: (bool) enable additional logging when using tensorboard WARNING: this logging can take a lot of space quickly """ def __init__(self, policy, env, expert_dataset=None, hidden_size_adversary=100, adversary_entcoeff=1e-3, g_step=3, d_step=1, d_stepsize=3e-4, verbose=0, _init_setup_model=True, **kwargs): super().__init__(policy, env, verbose=verbose, _init_setup_model=False, **kwargs) self.using_gail = True self.expert_dataset = expert_dataset self.g_step = g_step self.d_step = d_step self.d_stepsize = d_stepsize self.hidden_size_adversary = hidden_size_adversary self.adversary_entcoeff = adversary_entcoeff if _init_setup_model: self.setup_model()
[docs] def learn(self, total_timesteps, callback=None, log_interval=100, tb_log_name="GAIL", reset_num_timesteps=True): assert self.expert_dataset is not None, "You must pass an expert dataset to GAIL for training" return super().learn(total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps)