def __init__(self, actions, discount, featureExtractor, explorationProb,
stepSize, threshold, decay, maxGradient,
num_consecutive_random_actions):
"""
:note: please see parent class for params not described here
"""
super(SARSALambdaLearningAlgorithm,
self).__init__(actions, discount, featureExtractor,
explorationProb, stepSize, maxGradient,
num_consecutive_random_actions)
self.eligibility_traces = EligibilityTraces(threshold, decay)
def __init__(self, actions, featureExtractor, discount, explorationProb,
stepSize, decay, threshold):
super(SARSALambdaLearningAlgorithm,
self).__init__(actions, featureExtractor, discount,
explorationProb, stepSize)
self.threshold = threshold
self.decay = decay
self.eligibility_traces = EligibilityTraces(threshold, decay)
self.name = "SARSALambda"
self.maxFeatVectorNorm = 1
self.firstReward = 0
self.sawFirst = False
from q_learning import QLearning
from SARSA import SARSALearning
from eligibility_traces import EligibilityTraces
from function_approximation import FApprox
from mountain_cart import run_methods, self_iterate
import pickle
if __name__ == "__main__":
# Initialize a method
methods = [
QLearning("MountainCar-v0", print_progress=False),
SARSALearning("MountainCar-v0", print_progress=False),
FApprox("MountainCar-v0", print_progress=False),
EligibilityTraces("MountainCar-v0", print_progress=False)
]
# Run the tests
run_methods(methods)
method = methods[0]
method.q_table = pickle.load(
open("Best_Method_" + str(type(method).__name__) + ".p", "rb"))
method.evaluate()
method.display()
self_iterate(methods[0])
def resetTraces(self):
self.eligibility_traces = EligibilityTraces(self.threshold, self.decay)
def new_episode(self):
self.eligibility_traces = EligibilityTraces(1 - self.γ * self.λ)
self.ε *= self.ε_decay
self.episode += 1
self.episode_reward = 0
