
#Learning Algorithms 0=Q-Learning, 1=Q-Learning with Optimistic Initial Values
# 2= Q-Learning with Eligibility Trace, 3 = Q-Learning with Eligibility Trace and Optimistic Initial Value
emulator.learning.algorithm = 3

#State Policy 0 = Fixed Size States. 1= Variable Sized States determined by max actions per state.
emulator.learning.statepolicy = 1
emulator.learning.defaultstatesize = 400
emulator.learning.eligibility.decayrate = 0.75
emulator.learning.learningrate = 0.4
emulator.learning.discountfactor = 0.4
emulator.learning.defaultinitialvalue = 800
emulator.learning.initialvalue.policy = 1
emulator.learning.maxactionperstate = 64
emulator.learning.reward.alpha = 0.25
emulator.learning.reward.beta = 10.0
emulator.learning.reward.phi = 10.0
emulator.learning.reward.k = -0.20

emulator.learning.spawnthreshhold.use=true
emulator.learning.spawnthreshhold.lower = 500
emulator.learning.spawnthreshhold.upper = 50000000

# Selection policy 0=Greedy 1=E-Greedy
emulator.learning.selection.policy=1
emulator.learning.selection.epsilon=0.05

emulator.vm.cost.creation = 500
emulator.vm.thread.cost.commit=50
emulator.vm.thread.cost.squash=200


#VM Optimization
emulator.vm.optimization.space=false

#Learning Optimization: 0= 1 action regardless of depth
#1 = 1 from non-speculative, 1 from all speculative
emulator.learning.action.depthgranularity = 0

#Drop-Fast Algorithm - Penalises actions that are selected but cannot be executed
#The penalty rate is applied after the reward is transfered to an equivalen action
emulator.learning.dropfast.enabled=false
emulator.learning.dropfast.penalty=0.10
