def team_test(load=False):
game = Game(100, 100)
agent1 = Agent()
agent1b = Agent()
agent2 = QLearningAgent(epsilon=0.5, alpha=.2, alpha_decay=1.0)
agent2b = QLearningAgent(epsilon=0.5, alpha=.2, alpha_decay=1.0)
if load:
agent2.load_weights("team_weights.txt")
agent2.alpha = 0
# add agents
game.add_agent(agent1, (47, 30), 0)
game.add_agent(agent1b, (53, 30), 0)
game.add_agent(agent2, (0, 0), 1)
game.add_agent(agent2b, (0, 50), 1)
# simulate game
iterations = 10000
atexit.register(lambda: agent2.save_weights("team_weights.txt"))
game.start()
# agent2.debug = True
if not load: run_for_iterations(game, iterations)
game.add_listener(GraphicsListener(game))
agent2.epsilon = 0.01
agent2b.epsilon = 0.01
for _ in xrange(iterations):
game.loop()
time.sleep(.05)
def single_agent_test(load=False):
game = Game(100, 100)
agent = QLearningAgent(alpha=.2, alpha_decay=1.0, epsilon=.5)
if load:
agent.load_weights("single_agent_weights.txt")
agent.alpha = 0
agent.epsilon = .01
# add agents
game.add_agent(agent, (25, 15), 0)
# simulate game
iterations = 50000
atexit.register(lambda: agent.save_weights("single_agent_weights.txt"))
game.start()
# agent.debug = True
if not load: run_for_iterations(game, 10000)
game.add_listener(GraphicsListener(game))
for _ in xrange(iterations):
game.loop()
time.sleep(.05)
def battle():
game = Game(100, 100)
agent1 = QLearningAgent(epsilon=0.01, alpha=0, alpha_decay=1.0)
agent1b = QLearningAgent(epsilon=0.01, alpha=0, alpha_decay=1.0)
agent1c = HeuristicAgent()
agent1d = HeuristicAgent()
agent2 = QLearningAgent(epsilon=0.01, alpha=0, alpha_decay=1.0)
agent2b = QLearningAgent(epsilon=0.01, alpha=0, alpha_decay=1.0)
agent2c = QLearningAgent(epsilon=0.01, alpha=0, alpha_decay=1.0)
agent2d = QLearningAgent(epsilon=0.01, alpha=0, alpha_decay=1.0)
QFunction.load("learning_weights.txt")
# game.set_team_agent(CollaborativeTeamAgent(), 1)
agent2.alpha = 0
agent2b.alpha = 0
# add agents
game.add_agent(agent1, (0, 0), 0)
game.add_agent(agent1b, (100, 0), 0)
# game.add_agent(agent1c, (95,10), 0)
# game.add_agent(agent1d, (95,10), 0)
game.add_agent(agent2, (100,50), 1)
game.add_agent(agent2b, (0,50), 1)
# game.add_agent(agent2c, (100,50), 1)
# game.add_agent(agent2d, (100,50), 1)
# simulate game
iterations = 10000
game.start()
# game.add_listener(GraphicsListener(game))
# agent2.debug = True
for _ in xrange(iterations):
game.loop()
# time.sleep(.05)
print game.game_state.scores
def learning_enemies_test(load=False):
game = Game(100, 100)
agent1 = QLearningAgent(epsilon=0.2, alpha=.2, alpha_decay=1.0)
agent1b = HeuristicAgent()
agent2 = QLearningAgent(epsilon=0.2, alpha=.2, alpha_decay=1.0)
agent2b = HeuristicAgent()
if load:
QFunction.load("learning_weights.txt")
game.set_team_agent(CollaborativeTeamAgent(), 1)
agent2.alpha = 0
agent2b.alpha = 0
# add agents
game.add_agent(agent1, (5, 10), 0)
game.add_agent(agent1b, (95, 10), 0)
game.add_agent(agent2, (100, 50), 1)
game.add_agent(agent2b, (100, 50), 1)
# simulate game
iterations = 10000
if not load:
atexit.register(lambda: QFunction.save("learning_weights.txt"))
game.start()
# agent2.debug = True
if not load:
run_for_iterations(game, iterations)
agent2.debug = False
game.add_listener(GraphicsListener(game))
for _ in xrange(iterations):
game.loop()
time.sleep(.05)
def visualize_test():
game = Game(100, 100)
agent1 = QLearningAgent(epsilon=0.2, alpha=.2, alpha_decay=1.0)
agent1b = HeuristicAgent()
agent2 = QLearningAgent(epsilon=0.2, alpha=.2, alpha_decay=1.0)
agent2b = HeuristicAgent()
QFunction.load("learning_weights.txt")
game.set_team_agent(CollaborativeTeamAgent(), 1)
agent2.alpha = 0
agent2b.alpha = 0
# add agents
game.add_agent(agent1, (5, 10), 0)
game.add_agent(agent1b, (95, 10), 0)
game.add_agent(agent2, (100, 50), 1)
game.add_agent(agent2b, (100, 50), 1)
# simulate game
iterations = 10000
game.start()
graphics = GraphicsListener(game, clear_screen=False)
game.add_listener(
StateVisualization(game, 1, 0, root=graphics.master,
canvas=graphics.w))
game.add_listener(graphics)
for _ in xrange(iterations):
game.loop()
time.sleep(.05)
def main():
lake_env = gym.make('FrozenLake-v0', is_slippery=False)
coffee_env = coffeegame.CoffeeEnv()
taxi_env = gym.make('Taxi-v3').env
lake_agent = QLearningAgent(lake_env, 100, 20000, decay_rate=0.001, alpha=0.1, gamma=0.6, epsilon=1,
rendering_enabled=False)
coffee_agent = QLearningAgent(coffee_env, 100, 20000, decay_rate=0.01, alpha=0.1, gamma=0.6, epsilon=1,
rendering_enabled=False)
taxi_agent = QLearningAgent(taxi_env, 150, 100000, decay_rate=0.01, alpha=0.1, gamma=0.6, epsilon=1,
rendering_enabled=False)
lake_agent.learn("FrozenLake-v0 (non-slippery)")
# break
# averageOptima = sum(optima) / numGames
# averageRounds = total / numGames
# print "Average win rate: {0:.2f}%".format(100.0 * wins / total)
# print "Average number rounds before bust: {0:.2f}".format(1.0 * averageRounds)
# print "Average maximum money over games: {0:.2f}".format(1.0 * averageOptima)
# print "S.D. in maximum money over games: {0:.2f}".format((1.0 * sum([(i - averageOptima)**2 for i in optima]) / numGames)**0.5)
# print "S.D. in number of rounds over games: {0:.2f}".format((1.0 * sum([(i - averageRounds)**2 for i in numRounds]) / numGames)**0.5)
## Getting win rate for qlearner
trainingRounds = 1000000
file = "qLearningMitBetting1000"
args = {"flags": ["-np", "-cd"], "file": file}
# args = {"flags": ["-np"]}
player = QLearningAgent(0.8, 0.1, 1, **args)
game = Blackjack(8, player, **args)
rounds = 0
player.setTraining(True)
wins = 0
total = 0
while rounds < trainingRounds:
result = game.playRound()
if rounds % 1000 == 0:
print rounds
rounds += 1
# # Writes best action to file
# s = ""
# for j in xrange(20, 3, -1):
# for i in xrange(2, 12):
# Q learner iterations
i = multIndex(sys.argv, ["-qiter"])
if i != None and i != len(sys.argv) - 1:
trainingRounds = int(sys.argv[i + 1])
# Searching for which agent to use
player = Player(**args)
i = multIndex(sys.argv, ["-a", "-agent"])
if i != None and i != len(sys.argv) - 1:
if sys.argv[i + 1] == "random":
player = RandomAgent(**args)
elif sys.argv[i + 1] == "basic":
player = BasicStrategyAgent(**args)
elif sys.argv[i + 1] == "qlearning":
player = QLearningAgent(0.8, 0.1, 1, **args)
qlearning = True
game = Blackjack(8, player, **args)
rounds = 0
if qlearning:
# training cycle
player.setTraining(True)
while rounds < trainingRounds:
result = game.playRound()
if rounds % 1000 == 0:
print rounds
rounds += 1
player.setTraining(False)
player.setMoney(const.startingMoney)