def run(self):
log_dir = "./logs/bastille/Random_Vs_DeepQLearning/" + str(time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(RandomAgent(),
DeepQLearningAgent(8, 12),
checkpoint=100,
log_dir=log_dir).run(1000000))
def run():
log_dir = "./logs/bastilleMP/ReinforceWithMultipleTraj_Vs_TabularQLearning/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(
ReinforceClassicWithMultipleTrajectoriesAgent(8, 12),
TabularQLearningAgent(),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/DeepQLearning_Vs_TabularQLearning/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(DeepQLearningAgent(8, 12),
TabularQLearningAgent(),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/MOISMCTSWithRandomRollouts_Vs_TabularQLearning/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(MOISMCTSWithRandomRolloutsAgent(
100, SafeWindJammersRunner(RandomAgent(), RandomAgent())),
TabularQLearningAgent(),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/ReinforceClassicWithMultipleTrajectories_Vs_TabularQLearningAgent/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(
ReinforceClassicWithMultipleTrajectoriesAgent(8, 12),
TabularQLearningAgent(),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/Random_Vs_TabularQLearningAgent/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(RandomAgent(),
TabularQLearningAgent(),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/Random_Vs_RandomRollout_100/" + str(time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(RandomAgent(),
RandomRolloutAgent(
100,
SafeWindJammersRunner(
RandomAgent(), RandomAgent())),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/Random_Vs_DeepQLearning/" + str(time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(RandomAgent(),
DeepQLearningAgent(8, 12),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/Random_Vs_DoubleQLearning/" + str(time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(RandomAgent(),
DoubleQLearningAgent(),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/Random_Vs_ReinforceClassic/" + str(time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(RandomAgent(),
ReinforceClassicAgent(8, 12),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/Random_Vs_ReinforceClassicWithMultipleTrajectories/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(
RandomAgent(),
ReinforceClassicWithMultipleTrajectoriesAgent(8, 12),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/Random_Vs_PPOWithMultipleTrajectoriesMultiOutputs" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(
RandomAgent(),
PPOWithMultipleTrajectoriesMultiOutputsAgent(8, 12),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/Random_Vs_MOISMCTSWithRandomRollouts/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(RandomAgent(),
MOISMCTSWithRandomRolloutsAgent(
100,
SafeWindJammersRunner(
RandomAgent(), RandomAgent())),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/Random_Vs_MOISMCTSWithRandomRolloutsExpertThenApprentice/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(
RandomAgent(),
MOISMCTSWithRandomRolloutsExpertThenApprenticeAgent(
100, SafeWindJammersRunner(RandomAgent(), RandomAgent()), 8,
12),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/Random_Vs_MOISMCTSWithValueNetwork/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(RandomAgent(),
MOISMCTSWithValueNetworkAgent(
100,
SafeWindJammersRunner(
RandomAgent(), RandomAgent())),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/TabularQLearning_RandomRollout_100/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(TabularQLearningAgent(),
RandomRolloutAgent(
100,
SafeWindJammersRunner(
RandomAgent(), RandomAgent())),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/TabularQLearning_DeepQLearning/" + str(time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(TabularQLearningAgent(),
DeepQLearningAgent(8, 12),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/TabularQLearning_DoubleQLearning/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(TabularQLearningAgent(),
DoubleQLearningAgent(),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/TabularQLearning_ReinforceClassic/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(TabularQLearningAgent(),
ReinforceClassicAgent(8, 12),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/TabularQLearning_ReinforceClassicWithMultipleTrajectories/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(
TabularQLearningAgent(),
ReinforceClassicWithMultipleTrajectoriesAgent(8, 12),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/TabularQLearning_PPOWithMultipleTrajectoriesMultiOutputs" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(
TabularQLearningAgent(),
PPOWithMultipleTrajectoriesMultiOutputsAgent(8, 12),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/TabularQLearning_MOISMCTSWithRandomRollouts/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(TabularQLearningAgent(),
MOISMCTSWithRandomRolloutsAgent(
100,
SafeWindJammersRunner(
RandomAgent(), RandomAgent())),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/TabularQLearning_MOISMCTSWithRandomRolloutsExpertThenApprentice/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(
TabularQLearningAgent(),
MOISMCTSWithRandomRolloutsExpertThenApprenticeAgent(
100, SafeWindJammersRunner(RandomAgent(), RandomAgent()), 8,
12),
checkpoint=100,
log_dir=log_dir).run(100000))
log_dir = "./logs/bastilleMP/TabularQLearning_MOISMCTSWithValueNetwork/" + str(
time())
print(str(log_dir))
print(
TensorboardWindJammersRunner(TabularQLearningAgent(),
MOISMCTSWithValueNetworkAgent(
100,
SafeWindJammersRunner(
RandomAgent(), RandomAgent())),
checkpoint=100,
log_dir=log_dir).run(100000))
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")
if (self.replace_player1_with_commandline_after_similar_results is not None and
self.stuck_on_same_score >= self.replace_player1_with_commandline_after_similar_results):
self.agents = (CommandLineAgent(), self.agents[1])
self.stuck_on_same_score = 0
score_history = np.array((0, 0, 0))
self.execution_time = np.array((0.0, 0.0))
return tuple(score_history)
if __name__ == "__main__":
number = [1000, 10000, 100000, 1000000]
versus_name = ['RandomAgent', 'Tabular', 'DQN', 'DDQN', 'Reinforce', 'Reinforce A2C Style', 'PPO', 'MCTS']
versus_agent = [RandomAgent(),
TabularQLearningAgent(),
DeepQLearningAgent(9, 9),
DoubleDeepQLearningAgent(9, 9),
ReinforceClassicAgent(9, 9),
ReinforceClassicWithMultipleTrajectoriesAgent(9, 9),
PPOWithMultipleTrajectoriesMultiOutputsAgent(9, 9),
MOISMCTSWithValueNetworkAgent(9, 9, 2)]
versus = [versus_name, versus_agent]
for num in number:
for i in range(len(versus_name)):
with open("D:/DEEP_LEARNING/Reinforcement/TabularVS" + str(versus[0][i]) +"_NB_"+ str(num) + ".csv", 'w+') as f: #Ici change TabularVS par le nom de l'agent que tu lance contre tout le reste
print("New Fight" + str(versus[0][i]) + " " + str(num))
begin = time()
f.write("scoreJ1;execJ1;scoreJ2;execJ2;scoreEqual\n")
print(BasicTicTacToeRunner(TabularQLearningAgent(), #Ici tu remplace TabularQLearningAgent() par un autre agent (ex : DeepQLearningAgent(9,9)
