Beispiel #1
0
    return opts

  
if __name__ == '__main__':
  
  opts = parseOptions()

  ###########################
  # GET THE GRIDWORLD
  ###########################

  import gridworld
  mdpFunction = getattr(gridworld, "get"+opts.grid)
  mdp = mdpFunction()
  mdp.setLivingReward(opts.livingReward)
  mdp.setNoise(opts.noise)
  env = gridworld.GridworldEnvironment(mdp)

  
  ###########################
  # GET THE DISPLAY ADAPTER
  ###########################

  import textGridworldDisplay
  display = textGridworldDisplay.TextGridworldDisplay(mdp)
  if not opts.textDisplay:
    import graphicsGridworldDisplay
    display = graphicsGridworldDisplay.GraphicsGridworldDisplay(mdp, opts.gridSize, opts.speed)
  display.start()

  ###########################
Beispiel #2
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    return opts


if __name__ == '__main__':

    opts = parseOptions()

    ###########################
    # GET THE GRIDWORLD
    ###########################

    import gridworld
    mdpFunction = getattr(gridworld, "get" + opts.grid)
    mdp = mdpFunction()
    mdp.setLivingReward(opts.livingReward)
    mdp.setNoise(opts.noise)
    env = gridworld.GridworldEnvironment(mdp)

    ###########################
    # GET THE DISPLAY ADAPTER
    ###########################
    import textGridworldDisplay
    display = textGridworldDisplay.TextGridworldDisplay(mdp)
    if not opts.textDisplay:
        import graphicsGridworldDisplay
        display = graphicsGridworldDisplay.GraphicsGridworldDisplay(
            mdp, opts.gridSize, opts.speed)
    try:
        display.start()
    except KeyboardInterrupt:
        sys.exit(0)
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# ([email protected]) and Dan Klein ([email protected]).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel ([email protected]).

import mdp, util
import gridworld
from learningAgents import ValueEstimationAgent
import collections

# VOOR DEBUGGEN
mdpFunction = gridworld.getBridgeGrid
mdp = mdpFunction()
mdp.setLivingReward(0)
mdp.setNoise(0.2)
env = gridworld.GridworldEnvironment(mdp)
#a = ValueIterationAgent(mdp, 0.9, 100)


class ValueIterationAgent(ValueEstimationAgent):
    """
        * Please read learningAgents.py before reading this.*

        A ValueIterationAgent takes a Markov decision process
        (see mdp.py) on initialization and runs value iteration
        for a given number of iterations using the supplied
        discount factor.
    """
    def __init__(self, mdp, discount=0.9, iterations=100):
        """
Beispiel #4
0
def main(myargs):
    sys.argv = myargs.split()
    opts = parseOptions()

    ###########################
    # GET THE GRIDWORLD
    ###########################

    if opts.grid == 'VerticalBridgeGrid':
        opts.gridSize = 120

    import gridworld
    mdpFunction = getattr(gridworld, "get"+opts.grid)
    mdp = mdpFunction()
    mdp.setLivingReward(opts.livingReward)
    mdp.setNoise(opts.noise)
    env = gridworld.GridworldEnvironment(mdp)


    ###########################
    # GET THE DISPLAY ADAPTER
    ###########################

    import textGridworldDisplay
    display = textGridworldDisplay.TextGridworldDisplay(mdp)
    if not opts.textDisplay:
        import graphicsGridworldDisplay
        display = graphicsGridworldDisplay.GraphicsGridworldDisplay(mdp, opts.gridSize, opts.speed)
    try:
        display.start()
    except KeyboardInterrupt:
        sys.exit(0)

    ###########################
    # GET THE AGENT
    ###########################

    import valueIterationAgents, qlearningAgents
    a = None
    if opts.agent == 'value':
        a = valueIterationAgents.ValueIterationAgent(mdp, opts.discount, opts.iters)
    elif opts.agent == 'q':
        #env.getPossibleActions, opts.discount, opts.learningRate, opts.epsilon
        #simulationFn = lambda agent, state: simulation.GridworldSimulation(agent,state,mdp)
        gridWorldEnv = GridworldEnvironment(mdp)
        actionFn = lambda state: mdp.getPossibleActions(state)
        qLearnOpts = {'gamma': opts.discount,
                      'alpha': opts.learningRate,
                      'epsilon': opts.epsilon,
                      'actionFn': actionFn}
        a = qlearningAgents.QLearningAgent(**qLearnOpts)
    elif opts.agent == 'random':
        # # No reason to use the random agent without episodes
        if opts.episodes == 0:
            opts.episodes = 10
        class RandomAgent:
            def getAction(self, state):
                return random.choice(mdp.getPossibleActions(state))
            def getValue(self, state):
                return 0.0
            def getQValue(self, state, action):
                return 0.0
            def getPolicy(self, state):
                "NOTE: 'random' is a special policy value; don't use it in your code."
                return 'random'
            def update(self, state, action, nextState, reward):
                pass
        a = RandomAgent()
    else:
        if not opts.manual: raise 'Unknown agent type: '+opts.agent


    ###########################
    # RUN EPISODES
    ###########################
    # DISPLAY Q/V VALUES BEFORE SIMULATION OF EPISODES
    try:
        if not opts.manual and opts.agent == 'value':
            if opts.valueSteps:
                for i in range(opts.iters):
                    tempAgent = valueIterationAgents.ValueIterationAgent(mdp, opts.discount, i)
                    display.displayValues(tempAgent, message = "VALUES AFTER "+str(i)+" ITERATIONS")
                    display.pause()

            display.displayValues(a, message = "VALUES AFTER "+str(opts.iters)+" ITERATIONS")
            display.pause()
            display.displayQValues(a, message = "Q-VALUES AFTER "+str(opts.iters)+" ITERATIONS")
            display.pause()
    except KeyboardInterrupt:
        sys.exit(0)



    # FIGURE OUT WHAT TO DISPLAY EACH TIME STEP (IF ANYTHING)
    displayCallback = lambda x: None
    if not opts.quiet:
        if opts.manual and opts.agent == None:
            displayCallback = lambda state: display.displayNullValues(state)
        else:
            if opts.agent == 'random': displayCallback = lambda state: display.displayValues(a, state, "CURRENT VALUES")
            if opts.agent == 'value': displayCallback = lambda state: display.displayValues(a, state, "CURRENT VALUES")
            if opts.agent == 'q': displayCallback = lambda state: display.displayQValues(a, state, "CURRENT Q-VALUES")

    messageCallback = lambda x: printString(x)
    if opts.quiet:
        messageCallback = lambda x: None

    # FIGURE OUT WHETHER TO WAIT FOR A KEY PRESS AFTER EACH TIME STEP
    pauseCallback = lambda : None
    if opts.pause:
        pauseCallback = lambda : display.pause()

    # FIGURE OUT WHETHER THE USER WANTS MANUAL CONTROL (FOR DEBUGGING AND DEMOS)
    if opts.manual:
        decisionCallback = lambda state : getUserAction(state, mdp.getPossibleActions)
    else:
        decisionCallback = a.getAction

    # RUN EPISODES
    if opts.episodes > 0:
        print()
        print("RUNNING", opts.episodes, "EPISODES")
        print()
    returns = 0
    for episode in range(1, opts.episodes+1):
        returns += runEpisode(a, env, opts.discount, decisionCallback, displayCallback, messageCallback, pauseCallback, episode)
    if opts.episodes > 0:
        print()
        print("AVERAGE RETURNS FROM START STATE: "+str((returns+0.0) / opts.episodes))
        print()
        print()

    # DISPLAY POST-LEARNING VALUES / Q-VALUES
    if opts.agent == 'q' and not opts.manual:
        try:
            display.displayQValues(a, message = "Q-VALUES AFTER "+str(opts.episodes)+" EPISODES")
            display.pause()
            display.displayValues(a, message = "VALUES AFTER "+str(opts.episodes)+" EPISODES")
            display.pause()
        except KeyboardInterrupt:
            sys.exit(0)
    return opts


if __name__ == '__main__':

    opts = parseOptions()
    stepsLimit=9
    ###########################
    # GET THE GRIDWORLD
    ###########################
    import gridworld
    mdpFunction = getattr(gridworld, "get"+opts.grid)
    mdp = mdpFunction()
    mdp.setLivingReward(opts.livingReward)
    mdp.setNoise(0.1)
    env = gridworld.GridworldEnvironment(mdp)

    ###########################
    # GET THE DISPLAY ADAPTER
    ###########################

    import textGridworldDisplay
    display = textGridworldDisplay.TextGridworldDisplay(mdp)
    if not opts.textDisplay:
        import graphicsGridworldDisplay
        display = graphicsGridworldDisplay.GraphicsGridworldDisplay(mdp, opts.gridSize, opts.speed)
    try:
        display.start()
    except KeyboardInterrupt:
        sys.exit(0)