environment = DiscreteCartPoleEnvironment(maxSteps=200)
environment.conditions["actionNum"] = 2
environment.centerCart = False
experiment = APIExperiment(environment, agent)
# cut off last two state dimensions
# indexer = IndexingAdapter([0, 1], None)
# experiment.addAdapter(indexer)
# add normalization adapter
normalizer = NormalizingAdapter()
experiment.addAdapter(normalizer)
# add e-greedy exploration
explorer = EpsilonGreedyExplorer(0.3, episodeCount=500)
experiment.addAdapter(explorer)
experiment.setup()
explorer.epsilon = 0.5
explorer.decay = 0.9999
renderer = CartPoleRenderer()
for i in range(1):
valdata = experiment.evaluateEpisodes(100, visualize=False)
mean_return = mean([sum(v.rewards) for v in valdata])
print "mean return", mean_return
print "exploration", explorer.epsilon
print "mean ep. length", mean([len(e) for e in valdata])
print "num episodes", len(agent.history)
from numpy import *
import time
# create agent, environment, renderer, experiment
agent = SARSAAgent()
environment = DiscreteCartPoleEnvironment()
experiment = Experiment(environment, agent)
# add discretization adapter
discretizer = VQStateDiscretizationAdapter(30)
experiment.addAdapter(discretizer)
# add e-greedy exploration
explorer = EpsilonGreedyExplorer(0.3, episodeCount=1000)
experiment.addAdapter(explorer)
# force experiment setup now
experiment.setup()
for i in range(len(discretizer.stateVectors)):
plt.text(discretizer.stateVectors[i,0], discretizer.stateVectors[i,1], "%i"%i, bbox=dict(facecolor='green', alpha=0.5))
plt.xlim(-2.5, 2.5)
plt.ylim(-10, 10)
plt.show()
agent.forget()
explorer.epsilon = 0.3
# renderer = CartPoleRenderer()
# create agent, environment, renderer, experiment
agent = FQIAgent()
environment = DiscreteCartPoleEnvironment()
experiment = Experiment(environment, agent)
# cut off last two state dimensions
indexer = IndexingAdapter([0, 1], None)
experiment.addAdapter(indexer)
# add normalization adapter
normalizer = NormalizingAdapter()
experiment.addAdapter(normalizer)
# add e-greedy exploration
explorer = EpsilonGreedyExplorer(0.2, episodeCount=100)
experiment.addAdapter(explorer)
experiment.runEpisodes(10)
agent.forget()
explorer.decay = 0.999
renderer = CartPoleRenderer()
environment.renderer = renderer
renderer.start()
# run experiment
for i in range(100):
experiment.runEpisodes(1)
agent.learn()
