def solve(world, goalInWorld):
solution = GridWorld(world.width, world.height)
# print(solution.cells)
solution.cells = [Cell(cell) for cell in world.cells]
# print(solution.cells)
goal = solution.get(goalInWorld.col, goalInWorld.line)
goal.cost = 0
closed = []
opened = [goal]
# reopen = 0
while len(opened):
# print(enigmaAsStr(solution, goal))
# print("opened:", [(c.col, c.line) for c in opened])
cell = opened.pop()
closed.append(cell)
# for adj in solution.getAdjacentCells(cell):
for adj in solution.getAccessibleCells(cell):
# print("cell", cell, "has got a adj", adj)
if adj.reachable: # we ignore obstacles
direction = Direction.fromTo(adj, cell)
cost = cell.cost + direction.cost()
if adj.cost == -1: # or adj.cost > cost: # if not used yet
adj.direction = direction
adj.cost = cost
opened.append(adj)
if adj.cost > cost: # if not used yet
# reopen += 1
# print("reopen", reopen)
adj.direction = direction
adj.cost = cost
opened.append(adj)
return solution
def solve(world, goalInWorld):
solution = GridWorld(world.width, world.height)
# print(solution.cells)
solution.cells = [Cell(cell) for cell in world.cells]
# print(solution.cells)
goal = solution.get(goalInWorld.col, goalInWorld.line)
goal.cost = 0
closed = []
opened = [goal]
# reopen = 0
while len(opened):
# print(enigmaAsStr(solution, goal))
# print("opened:", [(c.col, c.line) for c in opened])
cell = opened.pop()
closed.append(cell)
# for adj in solution.getAdjacentCells(cell):
for adj in solution.getAccessibleCells(cell):
# print("cell", cell, "has got a adj", adj)
if adj.reachable: # we ignore obstacles
direction = Direction.fromTo(adj, cell)
cost = cell.cost + direction.cost()
if adj.cost == -1: # or adj.cost > cost: # if not used yet
adj.direction = direction
adj.cost = cost
opened.append(adj)
if adj.cost > cost: # if not used yet
# reopen += 1
# print("reopen", reopen)
adj.direction = direction
adj.cost = cost
opened.append(adj)
return solution
def setUp(self):
self.world = GridWorld(10, 10)
self.obstaclesProb = 0.2
self.world.addRandomObstacles(
math.floor(self.world.getLength() * self.obstaclesProb))
for cell in self.world.cells:
if cell.reachable:
self.goal = cell
break
def createSmallMaze(self):
#should be GridWorldSmall()
self.GridWorldGame = GridWorld((5, 5))
cols = self.GridWorldGame.size[0]
rows = self.GridWorldGame.size[1]
self.MAZE_X = cols * 32
self.MAZE_Y = rows * 32
FRAME = 8
self.START_X = (self.MAX_X - cols *
32) / 2 + FRAME #what happens if its not 0 in %32
self.START_Y = (self.MAX_Y - rows * 32) / 2 + FRAME
self.smileyPos = (self.START_X, self.START_Y)
def main():
env = GridWorld()
_, es1, ts1 = independentQLearning(env, lambda x: x < 100, 0)
qList, es2, ts2 = shareStateQLearning(env, lambda x: x < 100, 0)
iQL = plt.scatter(es1, ts1, c='red')
ssQL = plt.scatter(es2, ts2, c='blue')
iQL.set_label("Independent")
ssQL.set_label("5 Predators, 2 Prey, Share State")
plt.xlabel("Episodes")
plt.ylabel("Cumulative TimeSteps")
plt.legend()
plt.show()
env.simulateTrajectory(qList)
class Tests(unittest.TestCase):
def setUp(self):
self.world = GridWorld(10, 10)
self.obstaclesProb = 0.2
self.world.addRandomObstacles(math.floor(self.world.getLength() * self.obstaclesProb))
for cell in self.world.cells:
if cell.reachable:
self.goal = cell
break
def test_runs(self):
solution = solve(self.world, self.goal)
print(enigmaAsStr(solution, self.goal))
class Tests(unittest.TestCase):
def setUp(self):
self.world = GridWorld(10, 10)
self.obstaclesProb = 0.2
self.world.addRandomObstacles(
math.floor(self.world.getLength() * self.obstaclesProb))
for cell in self.world.cells:
if cell.reachable:
self.goal = cell
break
def test_runs(self):
solution = solve(self.world, self.goal)
print(enigmaAsStr(solution, self.goal))
def evaluate(goals, EQ):
env = GridWorld(goals=goals, T_states=T_states)
policy = EQ_P(EQ)
state = env.reset()
done = False
t = 0
G = 0
while not done and t < 100:
action = policy[state]
state_, reward, done, _ = env.step(action)
state = state_
G += reward
t += 1
return G
def openMDPGUI(self):
global w, g
if self.checkSettingValues():
self.master.destroy()
df = float(self.discFactor.get())
rews = list(map(lambda x: float(x.get()), self.rewValue))
probs = list(map(lambda x: float(x.get()), self.probValue))
w = GridWorld([[
GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID,
GridWorld.CELL_EXIT
],
[
GridWorld.CELL_VOID, GridWorld.CELL_WALL,
GridWorld.CELL_VOID, GridWorld.CELL_PIT
],
[
GridWorld.CELL_VOID, GridWorld.CELL_VOID,
GridWorld.CELL_VOID, GridWorld.CELL_VOID
]])
w.setDiscountFactor(df)
w.setRewards(rews[0], rews[1], rews[2])
w.setProbabilities(probs[0], probs[1], probs[2], probs[3])
g = MDPGUI(w)
def objectiveFunction(args):
learning_rate, min_epsilon, max_epsilon, epsilon_decay, discount_factor = args
num_of_episodes = 500
max_steps = 1000
environment = GridWorld()
agentQ = Q_Agent(environment,
epsilon=max_epsilon,
learning_rate=learning_rate,
discount_factor=discount_factor)
train(environment,
agentQ,
episodes=num_of_episodes,
max_steps_per_episode=max_steps,
min_epsilon=min_epsilon,
max_epsilon=max_epsilon,
epsilon_decay=epsilon_decay)
mean_reward = test(environment, agentQ, episodes=1000)
value_map = np.zeros((environment.height, environment.width))
for x in range(environment.height):
for y in range(environment.width):
q_values_of_state = agentQ.q_table[(x, y)]
maxValue = max(q_values_of_state.values())
value_map[x, y] = maxValue
if save == True:
utils.plotValueFunction(value_map,
os.path.join(save_path, 'heatmap.jpg'))
return -(mean_reward)
def setUp(self):
self.world = GridWorld(10, 10)
self.obstaclesProb = 0.2
self.world.addRandomObstacles(math.floor(self.world.getLength() * self.obstaclesProb))
for cell in self.world.cells:
if cell.reachable:
self.goal = cell
break
def gridworld(): ''' Create complete discrete environment for MDP modelling (InSpace Tiled), including Rewards and Transition probabilities''' w = GridWorld([[GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_EXIT, GridWorld.CELL_VOID, GridWorld.CELL_VOID], [GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID], [GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID], [GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_PIT, GridWorld.CELL_VOID], [GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID], [GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID], [GridWorld.CELL_VOID, GridWorld.CELL_PIT, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID], [GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID], [GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID], [GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID]], discountFactor = 1 ) w.setRewards(-0.04, -1, 1) w.setProbabilities(0.8, 0.1, 0.1, 0) # w.setDiscountFactor(0.6) return w
def baseTester():
''' runs a somewhat comprehensive test'''
try:
import QLearner as ql
except:
pass
#it is worth noting here that num_states can be 100 for any grid < 10x10 using the tuckerHash
#we need a new hash algo if we are to use a grid outside those parameters
baseKwargs = {'num_states':100, 'alpha':1.0, 'gamma':0.9, 'rar':0.5, 'radr':0.99, 'dyna':0, 'verbose':False}
'''
if you want to add your own test, add it here. I use a tuple to indicate one test it is:
(csv file, expected convergence iterations, kwarg modifier, test name)
'''
myTestList = [('testEasyWorld.csv', 800, 13,{}, 'easy test'),
('world01.csv', 7000, 16, {}, 'Tucker Test 1'),
('world02.csv', 7000, 17, {}, 'Tucker Test 2'),
('testGridWorld.csv', 5000, 20, {}, 'Leo Base Test'),
('testGridWorld.csv', 18000, 20, {'alpha':.2}, 'Test Learning Rate'),
('testEasyWorld.csv', 700, 13, {'rar': 0.05}, 'Test Exploration'),
('testEasyWorld.csv', 700, 13, {'radr': 0.8}, 'Test Exploration Decay'),
('testGridWorld.csv', 3000, 20, {'gamma':0.8}, 'Test Discount Rate'),
('testGridWorld.csv', 1100, 20, {'dyna':100}, 'Test Dyna'),
]
fdtest=myTestList[7:9]
#for test in myTestList:
for test in fdtest:
print '-------------------------------'
print test[4]
world = GridWorld(test[0])
testKwargs = copy(baseKwargs)
for k in test[3].keys():
testKwargs[k] = test[3][k]
print 'parameters %s' % str(testKwargs)
learner = ql.QLearner(**testKwargs)
print world.grid
myTester = QTester(world, learner)
nIter = test[1]
totalIter = nIter
lastPolicyLength = 0
#someone let me know if there's a better way to check for convergence time
while (totalIter < (test[1] * 1.4)):
myTester.nIter(nIter)
nIter = int(.05*test[1])
myPolicy = myTester.getPolicy()
policyLength = len(myPolicy)
totalIter += nIter
if (lastPolicyLength == policyLength) and (policyLength < 100):
print 'converged in approx %i iterations' % totalIter
print policyLength, myPolicy, test[2]
break
lastPolicyLength = policyLength
if (test[1]*1.2 >= totalIter) and (policyLength == test[2]):
print '*** TEST PASSED ***'
else:
print 'xxx TEST FAILED xxx'
def setUp(self):
self.n = 5
self.p = 1
self.gridworld = GridWorld(self.n, self.p)
self.go_right_policy = np.ones(self.n * self.n, dtype=int)
self.discount = 0.9
self.large_discount = 0.2
self.policy = np.array(
[['TERMINAL', 'RIGHT', 'RIGHT', 'RIGHT', 'TERMINAL'],
['RIGHT', 'RIGHT', 'RIGHT', 'RIGHT', 'UP'],
['RIGHT', 'RIGHT', 'RIGHT', 'RIGHT', 'UP'],
['RIGHT', 'RIGHT', 'RIGHT', 'RIGHT', 'UP'],
['RIGHT', 'RIGHT', 'RIGHT', 'RIGHT', 'UP']])
self.policy_large_discount = np.array(
[['TERMINAL', 'LEFT', 'RIGHT', 'RIGHT', 'TERMINAL'],
['UP', 'LEFT', 'RIGHT', 'RIGHT', 'UP'],
['UP', 'LEFT', 'RIGHT', 'RIGHT', 'UP'],
['UP', 'LEFT', 'RIGHT', 'RIGHT', 'UP'],
['UP', 'LEFT', 'RIGHT', 'RIGHT', 'UP']])
def __init__(self,
epsilon=0.01,
greedy=False,
alpha=0.1,
gamma=0.95,
visual=True,
goal=(10, 8),
agentPose=(1, 1, 'up'),
showTrial=True,
randomReset=False,
epsilonStrat=1,
epsilonFactor=500):
"""
gridWorld: GridWorld object
epsilon: value used for epsilon greedy search
alpha: step size
gamma: discount favtor
"""
self.actionValues = Counter()
self.epsilonFactor = epsilonFactor
self.randomReset = randomReset
self.epsilon = epsilon
self.greedy = greedy
self.epsilonStrat = epsilonStrat
self.goal = goal
self.Q = dict()
self.gridWorld = GridWorld(goal,
agentPose,
visual=visual,
showTrial=showTrial,
randomReset=randomReset)
self.actions = self.gridWorld.getActions()
self.Model = dict()
self.alpha = alpha
self.PriorityQueue = PriorityQueue()
self.gamma = gamma
self.exp = []
self.rewards = dict()
self.rewardNums = dict()
self.predecessors = defaultdict(set)
self.initQValues()
def start_grid_mdp():
"""
starts the program, restarts if the user wants to
"""
grid = load_grid(get_file_path())
world = GridWorld(grid)
move_costs = get_move_cost()
gamma = get_gamma()
eval_steps = get_evaluation_steps()
MDP(world, eval_steps, gamma, move_costs)
if start_again():
start_grid_mdp()
def buildBiasEngine(self):
"""
Simulates MDPs with varying bias to build a bias inference engine.
"""
print "Loading MDPs...\n"
# Unnecessary progress bar for terminal
bar = pyprind.ProgBar(len(self.test))
for i in self.test:
self.sims.append(
GridWorld(self.grid, i, self.discount, self.tau, self.epsilon))
bar.update()
print "\nDone loading MDPs..."
def openMDPGUI(self):
global w, g
if self.checkSettingValues():
self.master.destroy()
df = float(self.discFactor.get())
rews = list(map(lambda x: float(x.get()), self.rewValue))
probs = list(map(lambda x: float(x.get()), self.probValue))
w = GridWorld(
[
[GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_EXIT],
[GridWorld.CELL_VOID, GridWorld.CELL_WALL, GridWorld.CELL_VOID, GridWorld.CELL_PIT],
[GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID],
]
)
w.setDiscountFactor(df)
w.setRewards(rews[0], rews[1], rews[2])
w.setProbabilities(probs[0], probs[1], probs[2], probs[3])
g = MDPGUI(w)
import numpy as np
from matplotlib import pyplot as plt
import deepdish as dd
from GridWorld import GridWorld
from library import *
env = GridWorld()
T_states = [(3, 3), (3, 9), (9, 3), (9, 9), (1, 1), (1, 2), (1, 3), (1, 4),
(1, 5), (1, 7), (1, 8), (1, 9), (1, 10), (1, 11), (11, 1), (11, 2),
(11, 3), (11, 4), (11, 5), (11, 7), (11, 8), (11, 9), (11, 10),
(2, 1), (3, 1), (4, 1), (5, 1), (7, 1), (8, 1), (9, 1), (10, 1),
(2, 11), (3, 11), (4, 11), (5, 11), (6, 11), (8, 11), (9, 11),
(10, 11), (11, 11)]
###################################### Qs
BTasksQ = [[t] for t in T_states]
###################################### EQs
Bases = []
n = int(np.ceil(np.log2(len(T_states))))
m = (2**n) / 2
for i in range(n):
Bases.append([])
b = False
for j in range(0, 2**n):
if j >= len(T_states):
break
if b:
Bases[i].append(1) #1=True=rmax
else:
Bases[i].append(0) #0=False=rmin
if (j + 1) % m == 0:
from GridWorld import GridWorld
from GridWorld import GridWorldAdditive
from ValueIteration import ValueIteration
# Run Value Iteration in different Grid World environments
if __name__ == "__main__":
gamma = 0.9
print("Grid world Value Iteration with discounted rewards gamma = %.2f\n" % gamma)
terminals = {(0, 3): +1, (1, 3): -1}
gw = GridWorld((3, 4), 0.8, [(1, 1)], terminals)
vi = ValueIteration()
values = vi.valueIteration(gw, gamma)
gw.printValues(values)
qvalues = vi.getQValues(gw, values, gamma)
gw.printQValues(qvalues)
policy = vi.getPolicy(gw, values, gamma)
gw.printPolicy(policy)
reward = -0.01
print("Grid world Value Iteration with additive rewards = %.2f\n" % reward)
gwa = GridWorldAdditive((3, 4), 0.8, [(1, 1)], terminals, reward)
values = vi.valueIteration(gwa, 1, 100)
gwa.printValues(values)
qvalues = vi.getQValues(gwa, values, 1)
gwa.printQValues(qvalues)
policy = vi.getPolicy(gwa, values, 1)
gwa.printPolicy(policy)
reward = -0.04
print("Grid World with additive rewards = %.2f\n" % reward)
gwa = GridWorldAdditive((3, 4), 0.8, [(1, 1)], terminals, reward)
hlpStr = ("Markov Decision Process Examples\n"
" Examples:\n"
" gridworld 1: std grid world as the book (step cost -0.04, discount factor 1)\n"
" gridworld 2: low discount factor 0.6 (step cost -0.04)\n"
" gridworld 3: low step cost -0.01\n"
" gridworld 4: suicide mode (step cost -2)\n"
)
print(hlpStr)
exit()
if len(sys.argv) == 1: showhelp()
if sys.argv[1] == "gridworld":
w = GridWorld([[GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_EXIT],
[GridWorld.CELL_VOID, GridWorld.CELL_WALL, GridWorld.CELL_VOID, GridWorld.CELL_PIT],
[GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID]], discountFactor = 1 )
if len(sys.argv) < 3:
mdpc = MDPChooser()
elif sys.argv[2] == "1":
w.setRewards(-0.04, -1, 1)
w.setProbabilities(0.8, 0.1, 0.1, 0)
w.setDiscountFactor(1)
g = MDPGUI(w)
elif sys.argv[2] == "2":
w.setRewards(-0.04, -1, 1)
w.setProbabilities(0.8, 0.1, 0.1, 0)
w.setDiscountFactor(0.9)
g = MDPGUI(w)
elif sys.argv[2] == "3":
def __init__(self):
self.game = GridWorld( (5,5))
self.squareCountGrid = self.game.createSquareCount()
self.alpha = 0.1
self.gamma = 0.9
from GridWorld import GridWorld
g = GridWorld(3,4)
policy={
(0, 0):'R',
(0, 1):'R',
(0, 2):'R',
(1, 0):'U',
(1, 1):'U',
(1, 2):'U',
(1, 3):'U',
(2, 0):'R',
(2, 1):'R',
(2, 2):'U',
(2, 3):'L'
}
def print_policy(p,g):
for r in range(g.row):
print('------------------')
for c in range(g.col):
a = p.get((r,c),' ')
print(' %s |'%a, end="")
print("")
def print_value(V,g):
for r in range(g.row):
print('------------------')
for c in range(g.col):
v = V.get((r,c), 0)
t = 0
G = 0
while not done and t < 100:
action = policy[state]
state_, reward, done, _ = env.step(action)
state = state_
G += reward
t += 1
return G
for t in range(len(types)):
print("type: ", t)
# Learning universal bounds (min and max tasks)
env = GridWorld(goals=T_states, dense_rewards=not types[t][0])
EQ_max, _ = Goal_Oriented_Q_learning(env, maxiter=maxiter)
env = GridWorld(goals=T_states,
goal_reward=-0.1,
dense_rewards=not types[t][0])
EQ_min, _ = Goal_Oriented_Q_learning(env, maxiter=maxiter)
# Learning base tasks and doing composed tasks
goals = Bases[0]
goals = [[pos, pos] for pos in goals]
env = GridWorld(goals=goals,
dense_rewards=not types[t][0],
T_states=T_states if types[t][1] else goals)
A, stats1 = Goal_Oriented_Q_learning(
env, maxiter=maxiter, T_states=None if types[t][1] else T_states)
class TDL_solution:
def __init__(self):
self.game = GridWorld( (5,5))
self.squareCountGrid = self.game.createSquareCount()
self.alpha = 0.1
self.gamma = 0.9
def playTDLGame(self,startSquare, randomMove):
self.game.currentSquare = startSquare
keepPlaying = not self.game.gameOver()
squares_and_returns = [(self.game.currentSquare,0)]
while keepPlaying:
#policy
i = self.game.currentSquare[0]
j = self.game.currentSquare[1]
move = self.game.policyGrid[i][j]
if randomMove < np.random.rand():
moves = self.game.possibleMoves((i,j))
moves.remove(move)
if len(moves) > 0:
idx = np.random.randint(0,len(moves))
move = moves[idx]
#move
self.game.move(move)
i = self.game.currentSquare[0]
j = self.game.currentSquare[1]
theReturn = self.game.returnGrid[i][j]
squares_and_returns.append( (self.game.currentSquare,theReturn) )
keepPlaying = not self.game.gameOver()
G = 0
self.squares_and_values = []
for square , theReturn in reversed(squares_and_returns):
self.squares_and_values.append( (square,G) )
G = theReturn + self.game.gamma*G
#self.squares_and_values.reverse()
def playSarsa(self,startSquare, randomMove):
self.game.currentSquare = startSquare
keepPlaying = not self.game.gameOver()
while keepPlaying:
#policy
i1 = self.game.currentSquare[0]
j1 = self.game.currentSquare[1]
move = self.game.policyGrid[i1][j1]
if randomMove < np.random.rand():
moves = self.game.possibleMoves((i1,j1))
print( str(i1) + " " + str(j1) + " " + str(moves) + " " + str(move) )
moves.remove(move)
if len(moves) > 0:
idx = np.random.randint(0,len(moves))
move = moves[idx]
#move
self.game.move(move)
i2 = self.game.currentSquare[0]
j2 = self.game.currentSquare[1]
theReturn = self.game.returnGrid[i2][j2]
self.game.valueGrid[i1][j1] = self.game.valueGrid[i1][j1] + self.alpha*(theReturn + self.gamma*self.game.valueGrid[i2][j2]- self.game.valueGrid[i1][j1] )
keepPlaying = not self.game.gameOver()
def playQLearning(self,startSquare, randomMove):
self.game.currentSquare = startSquare
keepPlaying = not self.game.gameOver()
while keepPlaying:
#policy
i1 = self.game.currentSquare[0]
j1 = self.game.currentSquare[1]
move = self.game.policyGrid[i1][j1]
# we use the best move even if random runs over it
i3 = self.game.currentSquare[0]
j3 = self.game.currentSquare[1]
if randomMove < np.random.rand():
moves = self.game.possibleMoves((i1,j1))
print( str(i1) + " " + str(j1) + " " + str(moves) + " " + str(move) )
moves.remove(move)
if len(moves) > 0:
idx = np.random.randint(0,len(moves))
move = moves[idx]
#move
self.game.move(move)
i2 = self.game.currentSquare[0]
j2 = self.game.currentSquare[1]
theReturn = self.game.returnGrid[i2][j2]
self.game.valueGrid[i1][j1] = self.game.valueGrid[i1][j1] + self.alpha*(theReturn + self.gamma*self.game.valueGrid[i3][j3]- self.game.valueGrid[i1][j1] )
keepPlaying = not self.game.gameOver()
def updateValueGrid(self):
for t in range(len(self.squares_and_values) -1):
square , _ = self.squares_and_values[t]
nextSquare, value = self.squares_and_values[t+1]
i1 = square[0]
j1 = square[1]
i2 = nextSquare[0]
j2 = nextSquare[1]
self.game.valueGrid[i1][j1] = self.game.valueGrid[i1][j1] + self.alpha*(value + self.gamma*self.game.valueGrid[i2][j2]- self.game.valueGrid[i1][j1] )
def updatePolicyGrid(self):
#check if policy change
#hasChanged = False
#if bestMove is new set to true.
rows = self.game.size[0]
cols = self.game.size[1]
change = False
for i in range(rows):
for j in range(cols):
if self.game.policyGrid[i][j] in [0,1,2,3]:
self.game.currentSquare = (i,j)
oldMove = self.game.policyGrid[i][j]
self.game.policyGrid[i][j] = self.game.bestMove()
if oldMove != self.game.policyGrid[i][j]:
change = True
return change
def printGrids(self):
self.game.printPolicyGrid()
self.game.printReturnGrid()
self.game.printValueGrid()
from Evaluation import Evaluation from GridWorld import GridWorld from Learning import Learning # グリッドワールドの大きさを指定 row = 5 column = 5 LearningAgentSpan = 10 # 学習エージェントの寿命 LearningTimes = 100 # 学習回数 P = 5 # 報酬 T = 10 # 遡る数 EvaluationAgentSpan = 10 # 評価エージェントの寿命 EvaluationTimes = 100 # 試行回数 grid_world = GridWorld(row, column) grid_world.make_grid_world() learning = Learning(grid_world.get_grid_world(), row, column) learning.do_learning(LearningAgentSpan, LearningTimes, P, T) evaluation = Evaluation(learning.get_grid_world(), row, column) evaluation.evaluation(EvaluationAgentSpan, EvaluationTimes)
vehState = start
env_file = open("Environment.txt", "w")
gridWorld = CreateEnvironment()
gridWorld.create(env_file,
size_row='10',
size_col='10',
agent_row=str(vehState[0]),
agent_col=str(vehState[1]),
goal_row=str(goal[0]),
goal_col=str(goal[1]),
static_number='2',
static_list=[0, 3, 2, 4])
env_file = open("Environment.txt", "r")
text_in_file = env_file.readline()
print(text_in_file)
grid = GridWorld(text_in_file)
gw = grid.gridDefine()
#-------------------------------------------------------
# initialize agent class and uav class
Agent = agent(vehState)
# define a model dictionary, which maps user inputs of learning model names to learning model function
modelType = {
"random": Agent.predict_Random,
"standard": Agent.predict_Standard,
"NN": Agent.predict_NN
}
UAV = uav(vehState)
# initialize decision model (options = "random", "standard", or "NN")
model = "random" # will be a user input
import hashlib
import json
from GridWorld import GridWorld
import numpy as np
import copy
from matplotlib import pyplot as plt
import torch
from torch.nn.modules.loss import SmoothL1Loss
import torch.nn as nn
from torch.optim import Adam
import random
grid_world = GridWorld()
rewards_to_plot = []
stats_ax = None
rewards_ax = None
model = None
def init_gridworld(random_player=False, random_mines=False, maze=False):
global grid_world
grid_world = GridWorld(random_player, random_mines, maze)
class NeuralNetwork(nn.Module):
def __init__(self, iterations=500):
super(NeuralNetwork, self).__init__()
from GridWorld import GridWorld
from Robot import Robot
env = GridWorld("grid-small.txt")
env.print_map()
gamma = 0.9
start = [0, 0]
agent = Robot(env, gamma)
epochs = 500
decay = 0.99
rvm_max_iter = 500
max_step = 1000
epsilon = 1
epsilon_threshold = 0.001
verbose = True
verbose_iteration = 1
steps, rewards = agent.learn(epochs, decay, rvm_max_iter, max_step, epsilon, start, verbose, verbose_iteration)
path = agent.get_path(start)
print(path)
def __createEmptyPolicy(self):
"""we create a partial function that is undefined in all points"""
c, r = self.world.size
return [[(None if self.world.cellAt(x, y) == GridWorld.CELL_VOID else
GridWorld.randomAction()) for x in range(c)]
for y in range(r)]
self.drawUtilities(canvas)
self.drawQValues(canvas)
self.drawPolicy(canvas)
# ===========================================================================
# TEST
# ===========================================================================
if __name__ == '__main__':
w = GridWorld([[
GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID,
GridWorld.CELL_EXIT
],
[
GridWorld.CELL_VOID, GridWorld.CELL_WALL,
GridWorld.CELL_VOID, GridWorld.CELL_PIT
],
[
GridWorld.CELL_VOID, GridWorld.CELL_VOID,
GridWorld.CELL_VOID, GridWorld.CELL_VOID
]],
discountFactor=1)
w.setRewards(-0.04, -1, 1)
w.setProbabilities(0.8, 0.1, 0.1, 0)
print("GridWorld-----------")
print(w)
print("----------------")
print("\nPolicy----------")
p = Policy(w)
def init_gridworld(random_player=False, random_mines=False, maze=False):
global grid_world
grid_world = GridWorld(random_player, random_mines, maze)
# RL take action and get next state and reward
_, next_state_index, reward, done = env.step(action)
# RL choose action based on next state
next_action = RL.choose_action(str(next_state_index))
# RL learn from this transition (s, a, r, s, a) ==> Sarsa
RL.learn(str(state), action, reward, str(next_state_index), next_action)
# swap state and action
state = next_state_index
action = next_action
# break while loop when end of this episode
if done:
break
# end of game
print('game over')
env.destroy()
if __name__ == "__main__":
env = GridWorld()
RL = Sarsa(actions=list(range(env.n_actions)))
env.after(10000, update)
env.mainloop()
print(RL.q_table)
from GridWorld import GridWorld
g = GridWorld(3, 4)
policy = {
(0, 0): {
'R': 1
},
(0, 1): {
'R': 1
},
(0, 2): {
'R': 1
},
(1, 0): {
'U': 1
},
(1, 1): {
'U': 1
},
(1, 2): {
'U': 1
},
(1, 3): {
'U': 1
},
(2, 0): {
'R': 0.5,
'U': 0.5
},
(2, 1): {
'R': 1
import tensorflow as tf
from GridWorld import GridWorld
np.random.seed(20)
tf.set_random_seed(20)
MAX_EPISODE = 1000
MAX_EP_STEPS = 1000 # maximum time step in one episode
GAMMA = 0.9 # reward discount in TD error
lr_actor = 0.001
lr_critic = 0.01
grid_world_h = 5
grid_world_w = 5
env = GridWorld(grid_world_h, grid_world_w)
n_features = 2
n_actions = 4
class Actor(object):
def __init__(self, sess, n_features, n_actions, lr=0.001):
self.sess = sess
self.state = tf.placeholder(tf.float32, [1, n_features], "state")
self.action = tf.placeholder(tf.int32, None, "act")
self.td_error = tf.placeholder(tf.float32, None, "td_error")
with tf.variable_scope('Actor'):
state_layer = tf.layers.dense(
inputs=self.state,
self.world.draw(canvas)
self.drawUtilities(canvas)
self.drawQValues(canvas)
self.drawPolicy(canvas)
#===========================================================================
# TEST
#===========================================================================
if __name__ == '__main__':
w = GridWorld([[GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_EXIT, GridWorld.CELL_VOID, GridWorld.CELL_VOID],
[GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID],
[GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID],
[GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_PIT, GridWorld.CELL_VOID],
[GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID],
[GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID],
[GridWorld.CELL_VOID, GridWorld.CELL_PIT, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID],
[GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID],
[GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID],
[GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID, GridWorld.CELL_VOID]], discountFactor = 1 )
w.setRewards(-0.04, -1, 1)
w.setProbabilities(0.8, 0.1, 0.1, 0)
# w.setDiscountFactor(0.9)
# print("-GridWorld-")
# print(w)
# print("-----------")
print("\n---Policy---")
def __createEmptyPolicy(self): '''we create a partial function that is undefined in all points''' c, r = self.world.size return [ [ (None if self.world.cellAt(x,y) == GridWorld.CELL_VOID else GridWorld.randomAction()) for x in range(c) ] for y in range(r) ]
return -1
grid_world.is_visited[x][y] = 1
grid_world.dfs_route.append((x, y))
random.shuffle(adjacent_nodes)
for l in adjacent_nodes:
if grid_world.is_visited[l[0]][l[1]] == 0:
ret_val = random_dfs(grid_world, str(l[0]) + "," + str(l[1]))
if ret_val == -1:
grid_world.dfs_best_route.append((l[0], l[1]))
return -1
def run_dfs(grid_world):
# dfs(grid_world, grid_world.start_key)
random_dfs(grid_world, grid_world.start_key)
grid_world.dfs_best_route.append((grid_world.start_x, grid_world.start_y))
grid_world.dfs_best_route = grid_world.dfs_best_route[::-1]
grid_world = GridWorld()
Functions.create_obstacles_from_hex(grid_world)
# Functions.create_random_obstacles(grid_world, 0.205)
# Functions.create_fixed_obstacles(grid_world, 6)
grid_world.scan_grid_and_generate_graph()
grid_world.print_graph()
grid_world.create_grid_ui(grid_world.m, grid_world.n, (grid_world.start_x, grid_world.start_y),
(grid_world.end_x, grid_world.end_y), grid_world.obstacles)
run_dfs(grid_world)
grid_world.move_on_given_route()
tk.mainloop()
