import os, sys
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt

class RandomWalker():
	def __init__(self,state_interval=[0,1],step_interval=[-0.2,0.2],start_state=0.5,gamma=1,seed=13):
		self.dims=1
		self.state_min = state_interval[0]
		self.state_max = state_interval[1]
		self.step_min = step_interval[0]
		self.step_max = step_interval[1]
		self.start_state = start_state
		self.curr_state = self.start_state
		self.gamma = gamma
		self.seed = seed
		np.random.seed(seed)

	def takeAction(self,action=None):
		'''
		Return:
			reward
			True/False flag to indicate if episode terminated (True if episode terminated)
		'''
		if action==None:
			# random policy
			action = np.random.uniform(self.step_min,self.step_max)
		self.curr_state += action
		if self.curr_state<self.state_min or self.curr_state>self.state_max:
			return self.curr_state, True
		else:
			return 0, False

	def reset(self):
		self.curr_state = self.start_state

	def runEpisode_randomPolicy(self,num_steps=10,verbose=0):
		for s in range(num_steps):
			r,is_terminated = self.takeAction()
			if verbose:
				print(r,is_terminated,self.curr_state)
			if is_terminated:
				self.reset()
35baf6ee72165856cd14e2a5e1d97ef7ee2a390f
<mask token>


class RandomWalker:
    <mask token>

    def takeAction(self, action=None):
        """
		Return:
			reward
			True/False flag to indicate if episode terminated (True if episode terminated)
		"""
        if action == None:
            action = np.random.uniform(self.step_min, self.step_max)
        self.curr_state += action
        if (self.curr_state < self.state_min or self.curr_state > self.
            state_max):
            return self.curr_state, True
        else:
            return 0, False
    <mask token>

    def runEpisode_randomPolicy(self, num_steps=10, verbose=0):
        for s in range(num_steps):
            r, is_terminated = self.takeAction()
            if verbose:
                print(r, is_terminated, self.curr_state)
            if is_terminated:
                self.reset()

<mask token>


class RandomWalker:
    <mask token>

    def takeAction(self, action=None):
        """
		Return:
			reward
			True/False flag to indicate if episode terminated (True if episode terminated)
		"""
        if action == None:
            action = np.random.uniform(self.step_min, self.step_max)
        self.curr_state += action
        if (self.curr_state < self.state_min or self.curr_state > self.
            state_max):
            return self.curr_state, True
        else:
            return 0, False

    def reset(self):
        self.curr_state = self.start_state

    def runEpisode_randomPolicy(self, num_steps=10, verbose=0):
        for s in range(num_steps):
            r, is_terminated = self.takeAction()
            if verbose:
                print(r, is_terminated, self.curr_state)
            if is_terminated:
                self.reset()

<mask token>


class RandomWalker:

    def __init__(self, state_interval=[0, 1], step_interval=[-0.2, 0.2],
        start_state=0.5, gamma=1, seed=13):
        self.dims = 1
        self.state_min = state_interval[0]
        self.state_max = state_interval[1]
        self.step_min = step_interval[0]
        self.step_max = step_interval[1]
        self.start_state = start_state
        self.curr_state = self.start_state
        self.gamma = gamma
        self.seed = seed
        np.random.seed(seed)

    def takeAction(self, action=None):
        """
		Return:
			reward
			True/False flag to indicate if episode terminated (True if episode terminated)
		"""
        if action == None:
            action = np.random.uniform(self.step_min, self.step_max)
        self.curr_state += action
        if (self.curr_state < self.state_min or self.curr_state > self.
            state_max):
            return self.curr_state, True
        else:
            return 0, False

    def reset(self):
        self.curr_state = self.start_state

    def runEpisode_randomPolicy(self, num_steps=10, verbose=0):
        for s in range(num_steps):
            r, is_terminated = self.takeAction()
            if verbose:
                print(r, is_terminated, self.curr_state)
            if is_terminated:
                self.reset()

import os, sys
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt


class RandomWalker:

    def __init__(self, state_interval=[0, 1], step_interval=[-0.2, 0.2],
        start_state=0.5, gamma=1, seed=13):
        self.dims = 1
        self.state_min = state_interval[0]
        self.state_max = state_interval[1]
        self.step_min = step_interval[0]
        self.step_max = step_interval[1]
        self.start_state = start_state
        self.curr_state = self.start_state
        self.gamma = gamma
        self.seed = seed
        np.random.seed(seed)

    def takeAction(self, action=None):
        """
		Return:
			reward
			True/False flag to indicate if episode terminated (True if episode terminated)
		"""
        if action == None:
            action = np.random.uniform(self.step_min, self.step_max)
        self.curr_state += action
        if (self.curr_state < self.state_min or self.curr_state > self.
            state_max):
            return self.curr_state, True
        else:
            return 0, False

    def reset(self):
        self.curr_state = self.start_state

    def runEpisode_randomPolicy(self, num_steps=10, verbose=0):
        for s in range(num_steps):
            r, is_terminated = self.takeAction()
            if verbose:
                print(r, is_terminated, self.curr_state)
            if is_terminated:
                self.reset()

import os, sys
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt

class RandomWalker():
	def __init__(self,state_interval=[0,1],step_interval=[-0.2,0.2],start_state=0.5,gamma=1,seed=13):
		self.dims=1
		self.state_min = state_interval[0]
		self.state_max = state_interval[1]
		self.step_min = step_interval[0]
		self.step_max = step_interval[1]
		self.start_state = start_state
		self.curr_state = self.start_state
		self.gamma = gamma
		self.seed = seed
		np.random.seed(seed)

	def takeAction(self,action=None):
		'''
		Return:
			reward
			True/False flag to indicate if episode terminated (True if episode terminated)
		'''
		if action==None:
			# random policy
			action = np.random.uniform(self.step_min,self.step_max)
		self.curr_state += action
		if self.curr_state<self.state_min or self.curr_state>self.state_max:
			return self.curr_state, True
		else:
			return 0, False

	def reset(self):
		self.curr_state = self.start_state

	def runEpisode_randomPolicy(self,num_steps=10,verbose=0):
		for s in range(num_steps):
			r,is_terminated = self.takeAction()
			if verbose:
				print(r,is_terminated,self.curr_state)
			if is_terminated:
				self.reset()