introdution we are -what are we going to talk about
TRANSCRIPT
Introdution
•We are•-What are we going to talk about
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Analysis
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Analysis
• Elements of winning:– Build Order– Information Gathering– Macro– Micro
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Analysis
• Terran Tactics– Timing Attack– Pushing– Harassment
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Analysis
• Unit analysis– Marine– Vulture– Wraiths
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Baysian N
etw
orks
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Baysian N
etw
ork
• Choice of decision model– Bayesian Networks– Decision Trees
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Baysian N
etw
ork
• Spawn Prediction
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Baysian N
etw
ork
• Threat level prediction – tvt
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Potential Fields
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Potential Field In G
eneral
• Attractive behavior
• Repulsive behavior
• From vector to number
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Our
Potential Field
Function
• Behavior is determined by + or –• due vs de• (2de - due)
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Changed W
hen
Impl
eme
nting Potential Field
• All forces are positive• Forces are learned
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Reinforcem
ent Learning
• Generalization of Q-Learning
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Environm
ent Variab
les
• Distance to Ally• Distance from Current Tile to Ally• Distance to Center of Squad• Distance from Current Tile to Center of Squad• Distance to Enemy• Distance from Current Tile to Enemy• Distance to Cliff or Edge• Distance from Current Tile to Cliff or Edge
A State is defined as the combination of all the following characteristics:
Number of units
Health Lost
Damage Dealt
Number of Units
Time
Weapon’s Cool Down
Shooting Range
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Reinforcem
ent Learning
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Gen
eralization Form
ulas
• Q-Approximation
• Reward
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Updating
Rules
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Algorithm
• Image that i’m still doing
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TEST
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Buil
ding a
test
Figure of StarEdit – a Starcraft BroodWar map editor
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Base
case
Testing without Reinforcement learning, and potential fields
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Base
case
Testing with potential fields, but not reinforcement learning
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Will
it converge?
Which Alpha and Gamma values? How many iterations is needed?
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Will
it converge?
Running tests with different values
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Will
it converge?
Values Alpha 0,2 and Gamma 0,9 (30852)
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Will
it converge?
Values Alpha 0,4 and Gamma 0,6 (135936)
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Pre
dict spaw
n and build
order
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Conclusion