def run(self):
if self.opponent == "RandomAgent":
log_dir1 = self.log_dir_root + "DoubleQLearningAgent_VS_RandomAgent_" + self.time
print(log_dir1)
print(TensorboardTicTacToeRunner(DoubleQLearningAgent(),
RandomAgent(),
log_and_reset_score_history_threshold=10000,
log_dir=log_dir1).run(100000000))
elif self.opponent == "TabularQLearningAgent":
log_dir2 = self.log_dir_root + "DoubleQLearningAgent_VS_TabularQLearningAgent_" + self.time
print(log_dir2)
print(TensorboardTicTacToeRunner(DoubleQLearningAgent(),
TabularQLearningAgent(),
log_and_reset_score_history_threshold=10000,
log_dir=log_dir2).run(100000000))
elif self.opponent == "DeepQLearningAgent":
log_dir3 = self.log_dir_root + "DoubleQLearningAgent_VS_DeepQLearningAgent_" + self.time
print(log_dir3)
print(TensorboardTicTacToeRunner(DoubleQLearningAgent(),
DeepQLearningAgent(9, 9),
log_and_reset_score_history_threshold=10000,
log_dir=log_dir3).run(100000000))
elif self.opponent == "ReinforceClassicAgent":
log_dir4 = self.log_dir_root + "DoubleQLearningAgent_VS_ReinforceClassicAgent_" + self.time
print(log_dir4)
print(TensorboardTicTacToeRunner(DoubleQLearningAgent(),
ReinforceClassicAgent(9, 9),
log_and_reset_score_history_threshold=10000,
log_dir=log_dir4).run(100000000))
elif self.opponent == "ReinforceClassicWithMultipleTrajectoriesAgent":
log_dir5 = self.log_dir_root + "DoubleQLearningAgent_VS_ReinforceClassicWithMultipleTrajectoriesAgent_" + self.time
print(log_dir5)
print(TensorboardTicTacToeRunner(DoubleQLearningAgent(),
ReinforceClassicWithMultipleTrajectoriesAgent(9, 9),
log_and_reset_score_history_threshold=10000,
log_dir=log_dir5).run(100000000))
elif self.opponent == "PPOWithMultipleTrajectoriesMultiOutputsAgent":
log_dir6 = self.log_dir_root + "DoubleQLearningAgent_VS_PPOWithMultipleTrajectoriesMultiOutputsAgent_" + self.time
print(log_dir6)
print(TensorboardTicTacToeRunner(DoubleQLearningAgent(),
PPOWithMultipleTrajectoriesMultiOutputsAgent(9, 9),
log_and_reset_score_history_threshold=10000,
log_dir=log_dir6).run(100000000))
elif self.opponent == "MOISMCTSWithRandomRolloutsAgent":
log_dir7 = self.log_dir_root + "DoubleQLearningAgent_VS_MOISMCTSWithRandomRolloutsAgent_" + self.time
print(log_dir7)
print(TensorboardTicTacToeRunner(DoubleQLearningAgent(),
MOISMCTSWithRandomRolloutsAgent(100,
SafeTicTacToeRunner(RandomAgent(),
RandomAgent())),
log_and_reset_score_history_threshold=10000,
log_dir=log_dir7).run(1000000000))
elif self.opponent == "MOISMCTSWithRandomRolloutsExpertThenApprenticeAgent":
log_dir8 = self.log_dir_root + "DoubleQLearningAgent_VS_MOISMCTSWithRandomRolloutsExpertThenApprenticeAgent_" + self.time
print(log_dir8)
print(TensorboardTicTacToeRunner(DoubleQLearningAgent(),
MOISMCTSWithRandomRolloutsExpertThenApprenticeAgent(100,
SafeTicTacToeRunner(
RandomAgent(),
RandomAgent()),9,9),
log_and_reset_score_history_threshold=10000,
log_dir=log_dir8).run(1000000000))
elif self.opponent == "MOISMCTSWithValueNetworkAgent":
log_dir9 = self.log_dir_root + "DoubleQLearningAgent_VS_MOISMCTSWithValueNetworkAgent_" + self.time
print(log_dir9)
print(TensorboardTicTacToeRunner(DoubleQLearningAgent(),
MOISMCTSWithValueNetworkAgent(100,
SafeTicTacToeRunner(RandomAgent(),
RandomAgent())),
log_and_reset_score_history_threshold=10000,
log_dir=log_dir9).run(1000000000))
elif self.opponent == "DoubleQLearningAgent":
log_dir10 = self.log_dir_root + "DoubleQLearningAgent_VS_DoubleQLearningAgent_" + self.time
print(log_dir10)
print(TensorboardTicTacToeRunner(DoubleQLearningAgent(),
DoubleQLearningAgent(),
log_and_reset_score_history_threshold=10000,
log_dir=log_dir9).run(1000000000))
elif self.opponent == "RandomRolloutAgent":
nb_rollouts = 3
log_dir11 = self.log_dir_root + "RandomAgent_VS_RandomRolloutAgent(" + str(nb_rollouts) + ")_" + self.time
print(log_dir11)
print(TensorboardTicTacToeRunner(RandomAgent(),
RandomRolloutAgent(nb_rollouts,
SafeTicTacToeRunner(
RandomAgent(),
RandomAgent())),
log_and_reset_score_history_threshold=10000,
log_dir=log_dir11).run(1000000000))
else:
print("Unknown opponent")
# log_and_reset_score_history_threshold=10000,
# log_dir=log_dir).run(1000000000))
#
# log_dir = "./logs/Random_vs_all/RandomAgent_VS_MOISMCTSWithRandomRolloutsExpertThenApprenticeAgent_" + str(time())
# print(log_dir)
# print(TensorboardTicTacToeRunner(RandomAgent(),
# MOISMCTSWithRandomRolloutsExpertThenApprenticeAgent(100, SafeTicTacToeRunner(
# RandomAgent(), RandomAgent()),9,9),
# log_and_reset_score_history_threshold=10000,
# log_dir=log_dir).run(1000000000))
#
log_dir = "./logs/Random_vs_all/RandomAgent_VS_MOISMCTSWithValueNetworkAgent_" + str(time())
print(log_dir)
print(TensorboardTicTacToeRunner(RandomAgent(),
MOISMCTSWithValueNetworkAgent(100,"1",
SafeTicTacToeRunner(RandomAgent(), RandomAgent())),
log_and_reset_score_history_threshold=10000,
log_dir=log_dir).run(1000000000))
# AGENTS EXAMPLES :
# CommandLineAgent()
# RandomAgent()
# RandomRolloutAgent(3, SafeTicTacToeRunner(RandomAgent(), RandomAgent()))
# TabularQLearningAgent()
# DeepQLearningAgent(9,9)
# ReinforceClassicAgent(9,9)
# ReinforceClassicWithMultipleTrajectoriesAgent(9,9)
# PPOWithMultipleTrajectoriesMultiOutputsAgent(9,9)
# MOISMCTSWithRandomRolloutsAgent(100, SafeTicTacToeRunner(RandomAgent(), RandomAgent()))
# MOISMCTSWithRandomRolloutsExpertThenApprenticeAgent(100, SafeTicTacToeRunner(RandomAgent(), RandomAgent()))
# MOISMCTSWithValueNetworkAgent(100, SafeTicTacToeRunner(RandomAgent(), RandomAgent()))