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import random

class Action(object):
	"""
	Implementation of an action.

	optimistic: determines whether or not Q is initialized optimistically
	"""
	def __init__(self, optimistic=False):
		# Current value estimation for this action

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import gym
import gym_bandits
from Agents import GreedyAgent, EpsilonGreedyAgent
import matplotlib.pyplot as plt

# env = gym.make("BanditTwoArmedDeterministicFixed-v0")
env = gym.make("BanditTenArmedUniformDistributedReward-v0")

greedy_agent = GreedyAgent(env)
e_greedy_agent = EpsilonGreedyAgent(env, 0.1)

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import gym
from gym import Env
from gym.spaces import Discrete
import random

class NonStationaryBanditEnv(Env):
    """
    Non-Stationary 5-arm Bandit Environment

    At any given time, the best action returns a reward of 1

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import random

class Action(object):
	"""
	Implementation of an action.

	optimistic: determines whether or not Q is initialized optimistically
	"""
	def __init__(self, optimistic=False):
		# Current value estimation for this action

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import random
import numpy as np

class Action(object):
	"""
	Implementation of an action.

	optimistic: determines whether or not Q is initialized optimistically
	"""
	def __init__(self, optimistic=False):

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from Agents import UCBAgent
import matplotlib.pyplot as plt
from NonStationaryBanditEnv import NonStationaryBanditEnv
import numpy as np

env = NonStationaryBanditEnv()

def testUCBAgent(c):
	ucb_agent = UCBAgent(env, 0.1, c)
	num_episodes = 10000

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import random
import numpy as np

class Action(object):
	"""
	Implementation of an action.

	optimistic: determines whether or not Q is initialized optimistically
	"""
	def __init__(self, optimistic=False):

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from Agents import GradientBanditAgent
import matplotlib.pyplot as plt
from NonStationaryBanditEnv import NonStationaryBanditEnv
import numpy as np

env = NonStationaryBanditEnv()

def testGBanditAgent(alpha):
	g_bandit_agent = GradientBanditAgent(env, alpha)
	num_episodes = 10000

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import gym
from gym import Env
from gym.spaces import Discrete, Box
import random
import numpy as np
import pprint

class GridWorldEnv(Env):
    """
	Simple 4x4 GridWorld Env from figure 4.1 in the RL textbook

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import numpy as np
import pprint

class State(object):
    """
    Represents a state or a point in the grid.

    coord: coordinate in grid world
    """
    def __init__(self, coord, is_terminal):