# Path: pyrl/agents/models/batch_model.py
# class BatchModel(ModelLearner):
# class KNNBatchModel(BatchModel):
# class RandomForestBatchModel(BatchModel):
# class SVMBatchModel(BatchModel):
# class GaussianProcessBatchModel(BatchModel):
# class SVMDensityEstimator(object):
#     class JoinedDensityModel(demodel, main_model):
#     def __init__(self, **kwargs):
#     def model_init(self, numDiscStates, contFeatureRanges, numActions, rewardRange):
#     def randomize_parameters(self, **args):
#     def _update_density_estimator(self):
#     def _known_state(self, states, action):
#     def _known_value(self, states, action):
#     def _genregressor(self, isReward=False, isTermination=False):
#     def _genclassifier(self):
#     def normState(self, state):
#     def denormState(self, state):
#     def normStates(self, states):
#     def denormStates(self, states):
#     def gaussianDist(self, dist):
#     def isKnown(self, state, action):
#     def areKnown(self, states):
#     def getConfidence(self, states):
#     def fitFactorModel(self, model, X, Y, allow_iw=True):
#     def computeImpWeights(self, data):
#     def updateModel(self):
#     def sampleStateActions(self, num_requested):
#     def exploration_reward(self, state, known, rewards):
#     def model_termination(self, pterm, known):
#     def predictSet(self, states):
#     def predict(self, state, action):
#     def updateExperience(self, lastState, action, newState, reward):
#     def __init__(self, **kwargs):
#     def randomize_parameters(self, **args):
#     def _genregressor(self, isReward=False, isTermination=False):
#     def _genclassifier(self):
#     def _known_state(self, states, action):
#     def _known_value(self, states, action):
#     def __init__(self, **kwargs):
#     def randomize_parameters(self, **args):
#     def _genregressor(self, isReward=False, isTermination=False):
#     def _genclassifier(self):
#     def __init__(self, **kwargs):
#     def randomize_parameters(self, **args):
#     def _genregressor(self, isReward=False, isTermination=False):
#     def _genclassifier(self):
#     def __init__(self, **kwargs):
#     def model_init(self, numDiscStates, contFeatureRanges, numActions, rewardRange):
#     def randomize_parameters(self, **args):
#     def _genregressor(self, isReward=False, isTermination=False):
#     def _genclassifier(self):
#     def _known_state(self, states, action):
#     def _known_value(self, states, action):
#     def buildConf(self, model, X, Y, zscore=1.960):
#     def _update_density_estimator(self):
#     def __init__(self, **params):
#     def randomize_parameters(self, **args):
#     def _init_density_estimator(self):
#     def _update_density_estimator(self):
#     def _known_state(self, states, action):
#     def _known_value(self, states, action):
# def genDensityEstModel(demodel, main_model):
#         def randomize_parameters(self, **args):
# GPSVM = genDensityEstModel(SVMDensityEstimator, GaussianProcessBatchModel)
# SVM2 = genDensityEstModel(SVMDensityEstimator, SVMBatchModel)
# KNNSVM = genDensityEstModel(SVMDensityEstimator, KNNBatchModel)


from rlglue.agent.Agent import Agent
from rlglue.agent import AgentLoader as AgentLoader
from rlglue.types import Action
from rlglue.types import Observation
from rlglue.utils import TaskSpecVRLGLUE3
from pyrl.rlglue.registry import register_agent
from random import Random
from pyrl.agents.models import batch_model
from pyrl.agents.planners import fitted_qiteration
    from pyrl.agents.skeleton_agent import runAgent
import numpy
import copy
import skeleton_agent

# Author: Will Dabney




@register_agent
class ModelBasedAgent(skeleton_agent.skeleton_agent):
    """
    ModelBasedAgent provides a reinforcement learning agent which plans, using the planner class provided,
    over a model of the domain, learned by the model learning class provided. So, essentially this class is
    just a wrapper around the real functionality of the planner and modeling classes.
        """

    name = "Model Based Agent"


    def init_parameters(self):

# Path: pyrl/agents/models/batch_model.py
# class BatchModel(ModelLearner):
# class KNNBatchModel(BatchModel):
# class RandomForestBatchModel(BatchModel):
# class SVMBatchModel(BatchModel):
# class GaussianProcessBatchModel(BatchModel):
# class SVMDensityEstimator(object):
#     class JoinedDensityModel(demodel, main_model):
#     def __init__(self, **kwargs):
#     def model_init(self, numDiscStates, contFeatureRanges, numActions, rewardRange):
#     def randomize_parameters(self, **args):
#     def _update_density_estimator(self):
#     def _known_state(self, states, action):
#     def _known_value(self, states, action):
#     def _genregressor(self, isReward=False, isTermination=False):
#     def _genclassifier(self):
#     def normState(self, state):
#     def denormState(self, state):
#     def normStates(self, states):
#     def denormStates(self, states):
#     def gaussianDist(self, dist):
#     def isKnown(self, state, action):
#     def areKnown(self, states):
#     def getConfidence(self, states):
#     def fitFactorModel(self, model, X, Y, allow_iw=True):
#     def computeImpWeights(self, data):
#     def updateModel(self):
#     def sampleStateActions(self, num_requested):
#     def exploration_reward(self, state, known, rewards):
#     def model_termination(self, pterm, known):
#     def predictSet(self, states):
#     def predict(self, state, action):
#     def updateExperience(self, lastState, action, newState, reward):
#     def __init__(self, **kwargs):
#     def randomize_parameters(self, **args):
#     def _genregressor(self, isReward=False, isTermination=False):
#     def _genclassifier(self):
#     def _known_state(self, states, action):
#     def _known_value(self, states, action):
#     def __init__(self, **kwargs):
#     def randomize_parameters(self, **args):
#     def _genregressor(self, isReward=False, isTermination=False):
#     def _genclassifier(self):
#     def __init__(self, **kwargs):
#     def randomize_parameters(self, **args):
#     def _genregressor(self, isReward=False, isTermination=False):
#     def _genclassifier(self):
#     def __init__(self, **kwargs):
#     def model_init(self, numDiscStates, contFeatureRanges, numActions, rewardRange):
#     def randomize_parameters(self, **args):
#     def _genregressor(self, isReward=False, isTermination=False):
#     def _genclassifier(self):
#     def _known_state(self, states, action):
#     def _known_value(self, states, action):
#     def buildConf(self, model, X, Y, zscore=1.960):
#     def _update_density_estimator(self):
#     def __init__(self, **params):
#     def randomize_parameters(self, **args):
#     def _init_density_estimator(self):
#     def _update_density_estimator(self):
#     def _known_state(self, states, action):
#     def _known_value(self, states, action):
# def genDensityEstModel(demodel, main_model):
#         def randomize_parameters(self, **args):
# GPSVM = genDensityEstModel(SVMDensityEstimator, GaussianProcessBatchModel)
# SVM2 = genDensityEstModel(SVMDensityEstimator, SVMBatchModel)
# KNNSVM = genDensityEstModel(SVMDensityEstimator, KNNBatchModel)


# Path: pyrl/agents/modelbased.py
from rlglue.agent.Agent import Agent
from rlglue.agent import AgentLoader as AgentLoader
from rlglue.types import Action
from rlglue.types import Observation
from rlglue.utils import TaskSpecVRLGLUE3
from pyrl.rlglue.registry import register_agent
from random import Random
from pyrl.agents.models import batch_model
from pyrl.agents.planners import fitted_qiteration
    from pyrl.agents.skeleton_agent import runAgent
import numpy
import copy
import skeleton_agent


# Author: Will Dabney




@register_agent
class ModelBasedAgent(skeleton_agent.skeleton_agent):
    """
    ModelBasedAgent provides a reinforcement learning agent which plans, using the planner class provided,
    over a model of the domain, learned by the model learning class provided. So, essentially this class is
    just a wrapper around the real functionality of the planner and modeling classes.
        """

    name = "Model Based Agent"


    def init_parameters(self):

        model_class = self.params.setdefault('model_class', batch_model.KNNBatchModel)