def ml_vs_titfortat_iterate():
print("Start an _iterated_ game between MachineLearning and Titfortat")
prisoner_a = MachineLearning("A")
prisoner_a.qlearning.gamma = 0.9 # We deeply care about future rewards
prisoner_b = Titfortat("B")
game = Game(prisoner_a, prisoner_b)
game.play(10000)
print("last 10 moves: ", prisoner_a.actions[-10:])
def ml_vs_titfortat_iterate():
print("Start an _iterated_ game between MachineLearning and Titfortat")
prisoner_a = MachineLearning("A")
prisoner_a.qlearning.gamma = 0.9 # We deeply care about future rewards
prisoner_b = Titfortat("B")
game = Game(prisoner_a, prisoner_b)
game.play(10000)
print("last 10 moves: ", prisoner_a.actions[-10:])
def deepqlearning_vs_titfortat_iterate():
print("Start an iterated game between DeepQLearning and Titfortat")
agent_a = DeepQLearnerAdaptater(2, 1, decay=0.9, learning_rate=0.02, scope="dvt_i_a")
prisoner_a = MachineLearning("A", agent=agent_a)
prisoner_b = Titfortat("B")
game = Game(prisoner_a, prisoner_b)
game.play(10000)
print("Last 1000 moves:")
print("prisoner_a:", Counter(prisoner_a.actions[-1000:]))
print("prisoner_b:", Counter(prisoner_b.actions[-1000:]))
def ml_vs_ml_iterate():
print("Start an _iterated_ game between two MachineLearning bots")
prisoner_a = MachineLearning("A")
prisoner_a.qlearning.gamma = 0.9 # We care about future rewards
prisoner_b = MachineLearning("B")
prisoner_b.qlearning.gamma = 0.9 # We care about future rewards
game = Game(prisoner_a, prisoner_b)
game.play(10000)
print("last 10 moves of a: ", prisoner_a.actions[-10:])
print("last 10 moves of b: ", prisoner_b.actions[-10:])
def ml_vs_ml_iterate():
print("Start an _iterated_ game between two MachineLearning bots")
prisoner_a = MachineLearning("A")
prisoner_a.qlearning.gamma = 0.9 # We care about future rewards
prisoner_b = MachineLearning("B")
prisoner_b.qlearning.gamma = 0.9 # We care about future rewards
game = Game(prisoner_a, prisoner_b)
game.play(10000)
print("last 10 moves of a: ", prisoner_a.actions[-10:])
print("last 10 moves of b: ", prisoner_b.actions[-10:])
def deepqlearning_vs_deepqlearning_non_iterate():
print("Start an non-iterated game between DeepQLearning and DeepQLearning")
agent_a = DeepQLearnerAdaptater(2, 1, decay=0.0, learning_rate=0.02, scope="dvd_ni_a")
prisoner_a = MachineLearning("A", agent=agent_a)
agent_b = DeepQLearnerAdaptater(2, 1, decay=0.0, learning_rate=0.02, scope="dvd_ni_b")
prisoner_b = MachineLearning("B", agent=agent_b)
game = Game(prisoner_a, prisoner_b)
game.play(10000)
print("Last 1000 moves:")
print("prisoner_a:", Counter(prisoner_a.actions[-1000:]))
print("prisoner_b:", Counter(prisoner_b.actions[-1000:]))
def deepqlearning_vs_titfortat_iterate():
print("Start an iterated game between DeepQLearning and Titfortat")
agent_a = DeepQLearnerAdaptater(2,
1,
decay=0.9,
learning_rate=0.02,
scope="dvt_i_a")
prisoner_a = MachineLearning("A", agent=agent_a)
prisoner_b = Titfortat("B")
game = Game(prisoner_a, prisoner_b)
game.play(10000)
print('Last 1000 moves:')
print('prisoner_a:', Counter(prisoner_a.actions[-1000:]))
print('prisoner_b:', Counter(prisoner_b.actions[-1000:]))
def deepqlearning_vs_deepqlearning_non_iterate():
print("Start an non-iterated game between DeepQLearning and DeepQLearning")
agent_a = DeepQLearnerAdaptater(2,
1,
decay=0.0,
learning_rate=0.02,
scope="dvd_ni_a")
prisoner_a = MachineLearning("A", agent=agent_a)
agent_b = DeepQLearnerAdaptater(2,
1,
decay=0.0,
learning_rate=0.02,
scope="dvd_ni_b")
prisoner_b = MachineLearning("B", agent=agent_b)
game = Game(prisoner_a, prisoner_b)
game.play(10000)
print('Last 1000 moves:')
print('prisoner_a:', Counter(prisoner_a.actions[-1000:]))
print('prisoner_b:', Counter(prisoner_b.actions[-1000:]))
