from garage.tf.algos.vpg import VPG
from garage.tf.optimizers import LbfgsOptimizer


class ERWR(VPG):
    """Episodic Reward Weighted Regression [1].

    Note:
        This does not implement the original RwR [2]_ that deals with
        "immediate reward problems" since it doesn't find solutions
        that optimize for temporally delayed rewards.

        .. [1] Kober, Jens, and Jan R. Peters. "Policy search for motor
                primitives in robotics." Advances in neural information
                processing systems. 2009.
        .. [2] Peters, Jan, and Stefan Schaal. "Using reward-weighted
                regression for reinforcement learning of task space control.
                " Approximate Dynamic Programming and Reinforcement Learning,
                2007. ADPRL 2007. IEEE International Symposium on. IEEE, 2007.

    Args:
        env_spec (garage.envs.EnvSpec): Environment specification.
        policy (garage.tf.policies.base.Policy): Policy.
        baseline (garage.tf.baselines.Baseline): The baseline.
        scope (str): Scope for identifying the algorithm.
            Must be specified if running multiple algorithms
            simultaneously, each using different environments
            and policies.
        max_path_length (int): Maximum length of a single rollout.
        discount (float): Discount.
        gae_lambda (float): Lambda used for generalized advantage
            estimation.
        center_adv (bool): Whether to rescale the advantages
            so that they have mean 0 and standard deviation 1.
        positive_adv (bool): Whether to shift the advantages
            so that they are always positive. When used in
            conjunction with center_adv the advantages will be
            standardized before shifting.
        fixed_horizon (bool): Whether to fix horizon.
        pg_loss (str): A string from: 'vanilla', 'surrogate',
            'surrogate_clip'. The type of loss functions to use.
        lr_clip_range (float): The limit on the likelihood ratio between
            policies, as in PPO.
        max_kl_step (float): The maximum KL divergence between old and new
            policies, as in TRPO.
        optimizer (object): The optimizer of the algorithm. Should be the
            optimizers in garage.tf.optimizers.
        optimizer_args (dict): The arguments of the optimizer.
        policy_ent_coeff (float): The coefficient of the policy entropy.
            Setting it to zero would mean no entropy regularization.
        use_softplus_entropy (bool): Whether to estimate the softmax
            distribution of the entropy to prevent the entropy from being
            negative.
        use_neg_logli_entropy (bool): Whether to estimate the entropy as the
            negative log likelihood of the action.
        stop_entropy_gradient (bool): Whether to stop the entropy gradient.
        entropy_method (str): A string from: 'max', 'regularized',
            'no_entropy'. The type of entropy method to use. 'max' adds the
            dense entropy to the reward for each time step. 'regularized' adds
            the mean entropy to the surrogate objective. See
            https://arxiv.org/abs/1805.00909 for more details.
        name (str): The name of the algorithm.
    """

    def __init__(self,
                 env_spec,
                 policy,
                 baseline,
                 scope=None,
                 max_path_length=500,
                 discount=0.99,
                 gae_lambda=1,
                 center_adv=True,
                 positive_adv=True,
                 fixed_horizon=False,
                 pg_loss='vanilla',
                 lr_clip_range=0.01,
                 max_kl_step=0.01,
                 optimizer=None,
                 optimizer_args=None,
                 policy_ent_coeff=0.0,
                 use_softplus_entropy=False,
                 use_neg_logli_entropy=False,
                 stop_entropy_gradient=False,
                 entropy_method='no_entropy',
                 name='ERWR'):
        if optimizer is None:
            optimizer = LbfgsOptimizer
            if optimizer_args is None:
                optimizer_args = dict()
        super().__init__(
            env_spec=env_spec,
            policy=policy,
            baseline=baseline,
            scope=scope,
            max_path_length=max_path_length,
            discount=discount,
            gae_lambda=gae_lambda,
            center_adv=center_adv,
            positive_adv=positive_adv,
            fixed_horizon=fixed_horizon,
            pg_loss=pg_loss,
            lr_clip_range=lr_clip_range,
            max_kl_step=max_kl_step,
            optimizer=optimizer,
            optimizer_args=optimizer_args,
            policy_ent_coeff=policy_ent_coeff,
            use_softplus_entropy=use_softplus_entropy,
            use_neg_logli_entropy=use_neg_logli_entropy,
            stop_entropy_gradient=stop_entropy_gradient,
            entropy_method=entropy_method,
            name=name)
