def final(self, state):
"Called at the end of each game."
# call the super-class final method
ReinforcementGhostAgent.final(self, state)
# did we finish training?
if self.episodesSoFar == self.numTraining:
# you might want to print your weights here for debugging
"*** YOUR CODE HERE ***"
print(self.weights)
def final(self, state):
"Called at the end of each game."
# call the super-class final method
ReinforcementGhostAgent.final(self, state)
# did we finish training?
if self.episodesSoFar == self.numTraining:
# you might want to print your weights here for debugging
if self.agentIndex == 1:
self.save("agent1_dqn.h5")
def __init__(self, epsilon=0.05, gamma=0.8, alpha=0.2, numTraining=0, agentIndex=1, extractor='GhostIdentityExtractor', **args):
"You can initialize Q-values here..."
args['epsilon'] = epsilon
args['gamma'] = gamma
args['alpha'] = alpha
args['numTraining'] = numTraining
args['agentIndex'] = agentIndex
self.index = agentIndex
self.q_values = util.Counter()
self.featExtractor = util.lookup(extractor, globals())()
self.weights = util.Counter()
#self.weights["stay-away-from-pacman"] = 1000.0
ReinforcementGhostAgent.__init__(self, **args)
def final(self, state):
"Called at the end of each game."
# call the super-class final method
ReinforcementGhostAgent.final(self, state)
# did we finish training?
if self.episodesSoFar == self.numTraining:
# you might want to print your weights here for debugging
# for feature in self.features_dict.keys():
# print(str(len(self.features_dict[feature])) + '=' + str(len(self.weights_dict[feature])))
# steps = range(0, len(self.features_dict[feature]))
# plt.plot(steps, self.features_dict[feature], 'Feature value')
# plt.plot(steps, self.weights_dict[feature], 'Weight')
# plt.xlabel('Step')
# plt.title(feature)
# plt.show()
print(self.weights)
def __init__(self,
epsilon=0.05,
gamma=0.8,
alpha=0.2,
numTraining=0,
agentIndex=1,
extractor='GhostAdvancedExtractor',
**args):
"You can initialize Q-values here..."
args['epsilon'] = epsilon
args['gamma'] = gamma
args['alpha'] = alpha
args['numTraining'] = numTraining
args['agentIndex'] = agentIndex
self.index = agentIndex
self.q_values = util.Counter()
self.featExtractor = util.lookup(extractor, globals())()
self.weights = util.Counter()
self.weights_dict = {}
self.features_dict = {}
ReinforcementGhostAgent.__init__(self, **args)
def __init__(self,
epsilon=1,
gamma=0.95,
alpha=0.001,
numTraining=0,
agentIndex=1,
extractor='GhostIdentityExtractor',
**args):
"You can initialize Q-values here..."
args['epsilon'] = epsilon
args['gamma'] = gamma
args['alpha'] = alpha
args['numTraining'] = numTraining
args['agentIndex'] = agentIndex
self.index = agentIndex
self.memory = deque(maxlen=2000)
self.q_values = util.Counter()
self.act_dict = {0: 'North', 1: 'South', 2: 'West', 3: 'East'}
self.action_size = 4 #used for output layer in dqn
self.state_size = 12 #used for input layer in dqn
self.batch_size = 32 #used for model training
self.featExtractor = util.lookup(extractor, globals())()
self.weights = util.Counter()
self.learning_rate = alpha
self.gamma = gamma # discount rate
self.epsilon = epsilon # exploration rate
self.epsilon_min = 0.01
self.epsilon_decay = 0.995
# self.path='D:/SMU/Courses/Introduction to AI/MiniProject_v2/MiniProject/Pacman_MiniProject'
self.model = load_model('agent1_dqn.h5')
# self.model=self._build_model()
self.safety_distance = 12 # safety distance (capsule to pacman + ghost to pacman)
self.last_node_pacman_was_heading_towards = None
self.last_node_pacman_was_moving_away_from = None
self.last_action = None # store action for Qvalues since action is defined differently
#self.q_values = util.Counter()
# self.q_values = self.load_obj("agent"+str(agentIndex)+"Q") #load previous q values
self.segment_dic = {
1: [(1, 6), (1, 7), (1, 8)],
2: [(1, 4), (1, 3), (1, 2)],
3: [(2, 9), (3, 9), (4, 9), (4, 8)],
4: [(2, 5)],
5: [(2, 1), (3, 1), (4, 1), (4, 2)],
6: [(3, 6), (3, 7)],
7: [(3, 3), (3, 4)],
8: [(4, 5), (5, 5)],
9: [(5, 7)],
10: [(5, 3)],
11: [(6, 8), (6, 9), (7, 9), (8, 9), (9, 9), (10, 9), (11, 9),
(12, 9), (13, 9), (13, 8)],
12: [(6, 6)],
13: [(6, 4)],
14: [(6, 2), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (11, 1),
(12, 1), (13, 1), (13, 2)],
15: [(7, 7), (8, 7)],
16: [(7, 3), (8, 3), (9, 3), (10, 3), (11, 3), (12, 3)],
17: [(8, 5), (9, 5), (10, 5), (11, 5), (9, 6), (10, 6)],
18: [(11, 7), (12, 7)],
19: [(13, 6)],
20: [(13, 4)],
21: [(14, 7)],
22: [(14, 5), (15, 5)],
23: [(14, 3)],
24: [(15, 8), (15, 9), (16, 9), (17, 9)],
25: [(15, 2), (15, 1), (16, 1), (17, 1)],
26: [(16, 6), (16, 7)],
27: [(16, 3), (16, 4)],
28: [(17, 5)],
29: [(18, 8), (18, 7), (18, 6)],
30: [(18, 2), (18, 3), (18, 4)],
#node
31: [(1, 9)],
32: [(1, 5)],
33: [(1, 1)],
34: [(3, 5)],
35: [(4, 7)],
36: [(4, 3)],
37: [(6, 7)],
38: [(6, 5)],
39: [(6, 3)],
40: [(9, 7)],
41: [(10, 7)],
42: [(13, 7)],
43: [(13, 5)],
44: [(13, 3)],
45: [(15, 7)],
46: [(15, 3)],
47: [(16, 5)],
48: [(18, 9)],
49: [(18, 5)],
50: [(18, 1)]
}
self.choice_dic = {
1: (31, 32),
2: (32, 33),
3: (31, 35),
4: (32, 34),
5: (33, 36),
6: (34, 35),
7: (34, 36),
8: (34, 38),
9: (35, 37),
10: (36, 39),
11: (37, 42),
12: (37, 38),
13: (38, 39),
14: (39, 44),
15: (37, 40),
16: (39, 44),
17: (40, 41),
18: (41, 42),
19: (42, 43),
20: (43, 44),
21: (42, 45),
22: (43, 47),
23: (44, 46),
24: (45, 48),
25: (46, 50),
26: (45, 47),
27: (46, 47),
28: (47, 49),
29: (48, 49),
30: (49, 50),
31: (32, 35),
32: (31, 33, 34),
33: (32, 36),
34: (32, 35, 36, 38),
35: (31, 34, 37),
36: (33, 34, 39),
37: (35, 38, 40, 42),
38: (34, 37, 39),
39: (36, 38, 44),
40: (37, 41),
41: (40, 42),
42: (37, 41, 43, 45),
43: (42, 44, 47),
44: (39, 43, 46),
45: (42, 47, 48),
46: (44, 47, 50),
47: (43, 45, 46, 49),
48: (45, 49),
49: (47, 48, 50),
50: (46, 49)
}
ReinforcementGhostAgent.__init__(self, **args)