Game Theory
mottcr
Tournament_Images1.zipTournament-0/Tournament-0/t0-action_distribution.pdf
1 2 3 Action Index, a
0.0
0.1
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0.3
π i( a
) Empirical Strategy, FP v. FP, RPS
p1
p2
Tournament-0/Tournament-0/t0-action_learning_path.pdf
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0.25
0.50
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1.25 π i( a
) Learning Path, FP v. FP, RPS
π0(0)
π0(1)
π0(2)
π1(0)
π1(1)
π1(2)
Tournament-0/Tournament-0/t0-reward_distribution.pdf
−1 0 1 Reward
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0.5 P
r( R
= r ), r
Reward Distribution, FP v. FP, RPS p1
p2
Tournament-0/Tournament-0/t0-reward_history.pdf
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Reward History, FP v. FP, RPS p1
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Tournament-1/Tournament-1/t1-action_distribution.pdf
1 2 3 Action Index, a
0.0
0.1
0.2
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0.4
π i( a
) Empirical Strategy, eG v. UCB, RPS
p1
p2
Tournament-1/Tournament-1/t1-action_learning_path.pdf
0 200 400 Iteration
0.00
0.25
0.50
0.75
1.00
1.25 π i( a
) Learning Path, eG v. UCB, RPS
π0(0)
π0(1)
π0(2)
π1(0)
π1(1)
π1(2)
Tournament-1/Tournament-1/t1-reward_distribution.pdf
−1.0 −0.5 0.0 0.5 1.0 Reward
0.0
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P r( R
= r ), r
Reward Distribution, eG v. UCB, RPS p1
p2
Tournament-1/Tournament-1/t1-reward_history.pdf
0 200 400 Iteration
−1.0
−0.5
0.0
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1.0 r
Reward History, eG v. UCB, RPS p1
p2
