class MinerEnv:
def __init__(self, host, port):
self.socket = GameSocket(host, port)
self.state = State()
self.energy_pre = self.state.energy
self.score_pre = self.state.score #Storing the last score for designing the reward function
def start(self): #connect to server
self.socket.connect()
def end(self): #disconnect server
self.socket.close()
def send_map_info(self, request): #tell server which map to run
self.socket.send(request)
def reset(self): #start new game
try:
message = self.socket.receive() #receive game info from server
self.state.init_state(message) #init state
except Exception as e:
import traceback
traceback.print_exc()
def step(self, action): #step process
self.socket.send(action) #send action to server
try:
message = self.socket.receive() #receive new state from server
self.state.update_state(message) #update to local state
except Exception as e:
import traceback
traceback.print_exc()
# Functions are customized by client
def get_state(self):
# Building the map
channel_1 = np.full(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1], 0.05)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
obs_id, val = self.state.mapInfo.get_obstacle(i, j)
if obs_id == TreeID: # Tree
channel_1[i, j] = 0.3
if obs_id == TrapID: # Trap
channel_1[i, j] = 0.6
if obs_id == SwampID: # Tree
if abs(val) == -5:
channel_1[i, j] = 0.2
if abs(val) == -20:
channel_1[i, j] = 0.4
if abs(val) > 20:
channel_1[i, j] = 0.8
channel_2 = np.full(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1], 0.05)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if self.state.mapInfo.gold_amount(i, j) > 0:
if self.state.mapInfo.gold_amount(i, j) < 500:
channel_2[i, j] = 0.3
if 900 > self.state.mapInfo.gold_amount(i, j) >= 500:
channel_2[i, j] = 0.6
if self.state.mapInfo.gold_amount(i, j) >= 900:
channel_2[i, j] = 1
channel_3 = np.full(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1], 0.05)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if self.state.x in range(21) and self.state.y in range(9):
channel_3[self.state.x, self.state.y] = 1
X = []
Y = []
for player in self.state.players:
if player["playerId"] != self.state.id:
X.append(player["posx"])
Y.append(player["posy"])
channel_4 = np.full(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1], 0.05)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if X[0] in range(21) and Y[0] in range(9):
channel_4[X[0], Y[0]] = 1
channel_5 = np.full(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1], 0.05)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if X[1] in range(21) and Y[1] in range(9):
channel_5[X[1], Y[1]] = 1
channel_6 = np.full(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1], 0.05)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if X[2] in range(21) and Y[2] in range(9):
channel_6[X[2], Y[2]] = 1
channel_7 = np.full(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1], 0.05)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
channel_7[i, j] = float(self.state.energy / 50)
DQNState = np.dstack([
channel_1, channel_2, channel_3, channel_4, channel_5, channel_6,
channel_7
])
DQNState = np.rollaxis(DQNState, 2, 0)
return DQNState
def get_reward(self):
# Calculate reward
reward = 0
score_action = self.state.score - self.score_pre
self.score_pre = int(self.state.score)
if score_action > 0 and self.state.lastAction == 5:
reward += 6.25
if score_action <= 0 and self.state.lastAction == 5:
reward -= 2
obs_id, value = self.state.mapInfo.get_obstacle(
self.state.x, self.state.y)
if obs_id not in [1, 2, 3] and self.state.lastAction != 4:
reward += 0.5
if obs_id == TreeID: # Tree
reward -= 2
if obs_id == TrapID: # Trap
reward -= 2
if obs_id == SwampID: # Swamp
if abs(value) <= -5:
reward -= 0.5
if 15 <= abs(value) <= 40:
reward -= 4
if abs(value) > 40:
reward -= 6
# if self.state.mapInfo.is_row_has_gold(self.state.x):
# if self.state.lastAction in [2,3]:
# reward += 1
# else:
# reward += 0.5
# if self.state.mapInfo.is_column_has_gold(self.state.x):
# if self.state.lastAction in [0,1]:
# reward += 1
# else:
# reward += 0.5
if self.state.lastAction == 4 and self.state.energy > 40:
reward -= 4
if self.state.lastAction == 4:
reward += 1.75
if self.state.status == State.STATUS_ELIMINATED_WENT_OUT_MAP:
reward += -10
if self.state.status == State.STATUS_ELIMINATED_OUT_OF_ENERGY:
reward += -5
return np.sign(reward) * np.log(1 + abs(reward))
def check_terminate(self):
#Checking the status of the game
#it indicates the game ends or is playing
return self.state.status != State.STATUS_PLAYING
class MinerEnv:
def __init__(self, host, port):
self.socket = GameSocket(host, port)
self.state = State()
self.score_pre = self.state.score #Storing the last score for designing the reward function
def start(self): #connect to server
self.socket.connect()
def end(self): #disconnect server
self.socket.close()
def send_map_info(self, request): #tell server which map to run
self.socket.send(request)
def reset(self): #start new game
# Choosing a map in the list
# mapID = np.random.randint(1, 6) # Choosing a map ID from 5 maps in Maps folder randomly
mapID = 1
posID_x = np.random.randint(
MAP_MAX_X) # Choosing a initial position of the DQN agent on
# X-axes randomly
posID_y = np.random.randint(
MAP_MAX_Y
) # Choosing a initial position of the DQN agent on Y-axes randomly
# Creating a request for initializing a map, initial position, the initial energy, and the maximum number of steps of the DQN agent
request = ("map" + str(mapID) + "," + str(posID_x) + "," +
str(posID_y) + ",50,100")
# Send the request to the game environment (GAME_SOCKET_DUMMY.py)
self.send_map_info(request)
try:
message = self.socket.receive() #receive game info from server
print(message)
self.state.init_state(message) #init state
print(self.state.score)
except Exception as e:
import traceback
traceback.print_exc()
def step(self, action): #step process
self.socket.send(action) #send action to server
try:
message = self.socket.receive() #receive new state from server
#print("New state: ", message)
self.state.update_state(message) #update to local state
print(self.state.score)
except Exception as e:
import traceback
traceback.print_exc()
# Functions are customized by client
def get_state(self):
# Building the map
view = np.zeros(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1, 2],
dtype="float32")
self.gold_map = np.zeros(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1],
dtype=int)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if self.state.mapInfo.get_obstacle(i, j) == TreeID: # Tree
view[i, j, 0] = -20 * 1.0 / 20
# view[i, j, 0] = -TreeID
elif self.state.mapInfo.get_obstacle(i, j) == TrapID: # Trap
view[i, j, 0] = -10 * 1.0 / 20
# view[i, j, 0] = -TrapID
elif self.state.mapInfo.get_obstacle(i, j) == SwampID: # Swamp
view[i, j, 0] = self.state.mapInfo.get_obstacle_value(
i, j) * 1.0 / 20
# view[i, j, 0] = -SwampID
elif self.state.mapInfo.gold_amount(i, j) > 0:
view[i, j,
0] = self.state.mapInfo.gold_amount(i, j) * 1.0 / 100
self.gold_map[i, j] = self.state.mapInfo.gold_amount(
i, j) / 50
if self.state.status == 0:
view[self.state.x, self.state.y, 1] = self.state.energy
# for player in self.state.players:
# if player["playerId"] != self.state.id:
# view[player["posx"], player["posy"], 1] -= 1
# Convert the DQNState from list to array for training
DQNState = np.array(view)
return DQNState
def check_terminate(self):
return self.state.status != State.STATUS_PLAYING
class MinerEnv:
def __init__(self, host, port):
self.socket = GameSocket(host, port)
self.state = State()
self.score_pre = self.state.score #Storing the last score for designing the reward function
def start(self): #connect to server
self.socket.connect()
def end(self): #disconnect server
self.socket.close()
def send_map_info(self, request): #tell server which map to run
self.socket.send(request)
def reset(self): #start new game
self.state_x_pre = self.state.x
self.state_y_pre = self.state.y
self.last3position = []
self.Swamp_position = []
try:
message = self.socket.receive() #receive game info from server
self.state.init_state(message) #init state
except Exception as e:
import traceback
traceback.print_exc()
def step(self, action): #step process
self.state_x_pre = self.state.x
self.state_y_pre = self.state.y
self.last3position.append([self.state.x, self.state.y])
if len(self.last3position) > 3:
self.last3position.pop(0)
#print(self.last3position)
self.socket.send(action) #send action to server
try:
message = self.socket.receive() #receive new state from server
self.state.update_state(message) #update to local state
except Exception as e:
import traceback
traceback.print_exc()
# Functions are customized by client
def get_state(self):
# Building the map
view = np.zeros(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1],
dtype=int)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if self.state.mapInfo.get_obstacle(i, j) == TreeID: # Tree
view[i, j] = -TreeID
if self.state.mapInfo.get_obstacle(i, j) == TrapID: # Trap
view[i, j] = -TrapID
if self.state.mapInfo.get_obstacle(i, j) == SwampID: # Swamp
view[i, j] = -SwampID
if self.state.mapInfo.gold_amount(i, j) > 0:
view[i, j] = self.state.mapInfo.gold_amount(i, j)
DQNState = view.flatten().tolist(
) #Flattening the map matrix to a vector
# Add position and energy of agent to the DQNState
DQNState.append(self.state.x)
DQNState.append(self.state.y)
DQNState.append(self.state.energy)
#Add position of bots
for player in self.state.players:
if player["playerId"] != self.state.id:
DQNState.append(player["posx"])
DQNState.append(player["posy"])
#Convert the DQNState from list to array for training
DQNState = np.array(DQNState)
return DQNState
def get_reward2(self):
# Calculate reward
reward = 0
score_action = self.state.score - self.score_pre
self.score_pre = self.state.score
if score_action > 0:
#If the DQN agent crafts golds, then it should obtain a positive reward (equal score_action)
reward += score_action
#If the DQN agent crashs into obstacels (Tree, Trap, Swamp), then it should be punished by a negative reward
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == TreeID: # Tree
reward -= TreeID * 3 * randrange(1, 5)
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == TrapID: # Trap
reward -= TrapID * 3
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == SwampID: # Swamp
if [self.state.x,
self.state.y] in self.Swamp_position: # go to Swamp again
reward -= 15
else:
reward -= SwampID * 3 # first time go to swamp
self.Swamp_position.append([self.state.x, self.state.y])
if self.state.mapInfo.gold_amount(
self.state_x_pre, self.state_y_pre
) >= 50 and self.state.lastAction != 5: # in gold but don't craft
reward -= 10
if self.state.lastAction == 5 and score_action < 0: # not in gold but craft
reward -= 10
if len(self.last3position
) == 3 and self.state.lastAction != 5: # back to same position
if self.last3position[0] == self.last3position[2]:
reward -= 3
if self.last3position[1] == self.last3position[2]:
reward -= 3
if self.state.energy >= 45 and self.state.lastAction == 4:
reward -= 7
# if self.state.status == State.STATUS_PLAYING:
# reward += 0.5
# If out of the map, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_WENT_OUT_MAP:
reward += -40
#Run out of energy, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_OUT_OF_ENERGY:
reward += -20
# print ("reward",reward)
return reward
def get_reward(self):
# Calculate reward
reward = 0
score_action = self.state.score - self.score_pre
self.score_pre = self.state.score
if score_action > 0:
#If the DQN agent crafts golds, then it should obtain a positive reward (equal score_action)
reward += score_action / 50
#If the DQN agent crashs into obstacels (Tree, Trap, Swamp), then it should be punished by a negative reward
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == TreeID: # Tree
reward -= 0.03 * randrange(1, 5)
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == TrapID: # Trap
reward -= 0.06 * 3
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == SwampID: # Swamp
if [self.state.x,
self.state.y] in self.Swamp_position: # go to Swamp again
reward -= 0.15
else:
reward -= 0.05 # first time go to swamp
self.Swamp_position.append([self.state.x, self.state.y])
if self.state.mapInfo.gold_amount(
self.state_x_pre, self.state_y_pre
) >= 50 and self.state.lastAction != 5: # in gold but don't craft
reward -= 0.55
if self.state.lastAction == 5 and score_action < 0: # not in gold but craft
reward -= 0.55
if len(self.last3position
) == 3 and self.state.lastAction != 5: # back to same position
if self.last3position[0] == self.last3position[2]:
reward -= 0.1
if self.last3position[1] == self.last3position[2]:
reward -= 0.1
if self.state.energy >= 45 and self.state.lastAction == 4:
reward -= 0.3
# if self.state.status == State.STATUS_PLAYING:
# reward += 0.5
# If out of the map, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_WENT_OUT_MAP:
reward += -10
#Run out of energy, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_OUT_OF_ENERGY:
reward += -5
# print ("reward",reward)
return reward
def get_state_tensor(self, scale_map):
n = scale_map
view = np.zeros((n * (self.state.mapInfo.max_x + 1),
n * (self.state.mapInfo.max_y + 1), 6))
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if self.state.mapInfo.get_obstacle(
i, j) == TreeID: # Tree # trap map
view[n * i:n * i + n, n * j:n * j + n, 0] = -TreeID
if self.state.mapInfo.get_obstacle(
i, j) == TrapID: # Trap # trap map
view[n * i:n * i + n, n * j:n * j + n, 0] = -TrapID
if self.state.mapInfo.get_obstacle(
i, j) == SwampID: # Swamp # trap map
view[n * i:n * i + n, n * j:n * j + n, 0] = -SwampID
if self.state.mapInfo.gold_amount(i, j) > 0:
view[n * i:n * i + n,
n * j:n * j + n, 0] = self.state.mapInfo.gold_amount(
i, j) / 1000 ##/10 gold map
for stt, player in enumerate(self.state.players):
if player["playerId"] != self.state.id:
try:
if player["status"] not in [1, 2, 3]:
try:
view[n * player["posx"]:n * player["posx"] + n,
n * player["posy"]:n * player["posy"] + n,
stt + 1] = player["energy"] / 50
except:
view[n * player["posx"]:n * player["posx"] + n,
n * player["posy"]:n * player["posy"] + n,
stt + 1] = 1
except:
view[n * player["posx"]:n * player["posx"] + n,
n * player["posy"]:n * player["posy"] + n, stt] = 1
# print(self.state.players)
else:
try:
view[n * self.state.x:n * self.state.x + n,
n * self.state.y:n * self.state.y + n,
2] = self.state.energy / 50
except:
print('out of map')
DQNState = np.array(view)
return DQNState
def get_state3(self, limit):
# Building the map
view = np.zeros([limit * 2 + 1, limit * 2 + 1], dtype=int)
max_x, max_y = self.state.mapInfo.max_x, self.state.mapInfo.max_y
xlimit_below = np.clip(self.state.x - limit, 0, max_x) - np.clip(
self.state.x + limit - max_x, 0, limit)
xlimit_up = np.clip(self.state.x + limit, 0, max_x) + np.clip(
0 - self.state.x + limit, 0, limit)
ylimit_below = np.clip(self.state.y - limit, 0, max_y) - np.clip(
self.state.y + limit - max_y, 0, limit)
ylimit_up = np.clip(self.state.y + limit, 0, max_y) + np.clip(
0 - self.state.y + limit, 0, limit)
#print(xlimit_below, xlimit_up, ylimit_below, ylimit_up, self.state.x, self.state.y)
dmax, m, n, exist_gold = -1000, -5, 0.1, False
x_maxgold, y_maxgold = self.state.x, self.state.y
for i in range(max_x + 1):
for j in range(max_y + 1):
if self.state.mapInfo.gold_amount(i, j) >= 50:
exist_gold = True
d = m * ((self.state.x - i)**2 + (self.state.y - j)**
2) + n * self.state.mapInfo.gold_amount(i, j)
if d > dmax:
dmax = d
x_maxgold, y_maxgold = i, j # position of cell is nearest and much gold
if i in range(xlimit_below, xlimit_up + 1) and j in range(
ylimit_below, ylimit_up + 1):
if self.state.mapInfo.get_obstacle(i, j) == TreeID: # Tree
view[i - xlimit_below, j - ylimit_below] = -TreeID
if self.state.mapInfo.get_obstacle(i, j) == TrapID: # Trap
view[i - xlimit_below, j - ylimit_below] = -TrapID
if self.state.mapInfo.get_obstacle(i,
j) == SwampID: # Swamp
view[i - xlimit_below, j - ylimit_below] = -SwampID
if self.state.mapInfo.gold_amount(i, j) > 0:
view[i - xlimit_below, j -
ylimit_below] = self.state.mapInfo.gold_amount(
i, j) / 10
DQNState = view.flatten().tolist(
) #Flattening the map matrix to a vector
# Add position and energy of agent to the DQNState
DQNState.append(self.state.x - xlimit_below)
DQNState.append(self.state.y - ylimit_below)
DQNState.append(self.state.energy)
#Add position of bots
# for player in self.state.players:
# if player["playerId"] != self.state.id:
# DQNState.append(player["posx"])
# DQNState.append(player["posy"])
DQNState.append(self.state.x - x_maxgold)
DQNState.append(self.state.y - y_maxgold)
if exist_gold == False:
DQNState.append(0)
else:
DQNState.append(
self.state.mapInfo.gold_amount(x_maxgold, y_maxgold) / 10)
#Convert the DQNState from list to array for training
DQNState = np.array(DQNState)
return DQNState
def check_terminate(self):
#Checking the status of the game
#it indicates the game ends or is playing
return self.state.status != State.STATUS_PLAYING
class MinerEnv:
def __init__(self, host, port):
self.socket = GameSocket(host, port)
self.state = State()
self.score_pre = self.state.score #Storing the last score for designing the reward function
self.pos_x_pre = self.state.x
self.pos_y_pre = self.state.y
def start(self): #connect to server
self.socket.connect()
def end(self): #disconnect server
self.socket.close()
def send_map_info(self, request): #tell server which map to run
self.socket.send(request)
def reset(self): #start new game
try:
message = self.socket.receive() #receive game info from server
self.state.init_state(message) #init state
self.score_pre = self.state.score #Storing the last score for designing the reward function
self.pos_x_pre = self.state.x
self.pos_y_pre = self.state.y
except Exception as e:
import traceback
traceback.print_exc()
def step(self, action): #step process
self.socket.send(action) #send action to server
try:
message = self.socket.receive() #receive new state from server
self.state.update_state(message) #update to local state
except Exception as e:
import traceback
traceback.print_exc()
# Functions are customized by client
def get_state(self):
#Local view
view = np.zeros([5, 5])
for i in range(-2, 3):
for j in range(-2, 3):
index_x = self.state.x + i
index_y = self.state.y + j
if index_x < 0 or index_y < 0 or index_x >= self.state.mapInfo.max_x or index_y >= self.state.mapInfo.max_y:
view[2 + i, 2 + j] = -1
else:
if self.state.mapInfo.get_obstacle(index_x,
index_y) == TreeID:
view[2 + i, 2 + j] = -1
if self.state.mapInfo.get_obstacle(index_x,
index_y) == TrapID:
view[2 + i, 2 + j] = -1
if self.state.mapInfo.get_obstacle(index_x,
index_y) == SwampID:
view[2 + i, 2 + j] = -1
#Create the state
DQNState = view.flatten().tolist()
self.pos_x_gold_first = self.state.x
self.pos_y_gold_first = self.state.y
if len(self.state.mapInfo.golds) > 0:
self.pos_x_gold_first = self.state.mapInfo.golds[0]["posx"]
self.pos_y_gold_first = self.state.mapInfo.golds[0]["posy"]
DQNState.append(self.pos_x_gold_first - self.state.x)
DQNState.append(self.pos_y_gold_first - self.state.y)
#Convert the DQNState from list to array for training
DQNState = np.array(DQNState)
return DQNState
def get_reward(self):
# Calculate reward
reward = 0
goldamount = self.state.mapInfo.gold_amount(self.state.x, self.state.y)
if goldamount > 0:
reward += 10 #goldamount
#remove the gold
for g in self.socket.stepState.golds:
if g.posx == self.state.x and g.posy == self.state.y:
self.socket.stepState.golds.remove(g)
#If the DQN agent crashs into obstacels (Tree, Trap, Swamp), then it should be punished by a negative reward
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == TreeID: # Tree
reward -= 0.2
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == TrapID: # Trap
reward -= 0.2
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == SwampID: # Swamp
reward -= 0.2
dis_pre = np.sqrt((self.pos_x_pre - self.pos_x_gold_first)**2 +
(self.pos_y_pre - self.pos_y_gold_first)**2)
dis_curr = np.sqrt((self.state.x - self.pos_x_gold_first)**2 +
(self.state.y - self.pos_y_gold_first)**2)
if (dis_curr - dis_pre) <= 0: #Reducing the distance , reward ++
reward += 0.1
else:
reward -= 0.1
# If out of the map, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_WENT_OUT_MAP:
reward += -10
return reward
def check_terminate(self):
#Checking the status of the game
#it indicates the game ends or is playing
return self.state.status != State.STATUS_PLAYING
class MinerEnv:
def __init__(self, host, port):
self.socket = GameSocket(host, port)
self.state = State()
self.score_pre = (
self.state.score
) # Storing the last score for designing the reward function
def start(self): # connect to server
self.socket.connect()
def end(self): # disconnect server
self.socket.close()
def send_map_info(self, request): # tell server which map to run
self.socket.send(request)
def reset(self): # start new game
try:
message = self.socket.receive() # receive game info from server
self.state.init_state(message) # init state
except Exception as e:
import traceback
traceback.print_exc()
def step(self, action): # step process
self.socket.send(action) # send action to server
try:
message = self.socket.receive() # receive new state from server
self.state.update_state(message) # update to local state
except Exception as e:
import traceback
traceback.print_exc()
# Functions are customized by client
def get_state(self):
# Building the map
view = np.zeros(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1],
dtype=int)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if self.state.mapInfo.get_obstacle(i, j) == TreeID: # Tree
view[i, j] = -TreeID
if self.state.mapInfo.get_obstacle(i, j) == TrapID: # Trap
view[i, j] = -TrapID
if self.state.mapInfo.get_obstacle(i, j) == SwampID: # Swamp
view[i, j] = -SwampID
if self.state.mapInfo.gold_amount(i, j) > 0:
view[i, j] = self.state.mapInfo.gold_amount(i, j)
DQNState = view.flatten().tolist(
) # Flattening the map matrix to a vector
# Add position and energy of agent to the DQNState
DQNState.append(self.state.x)
DQNState.append(self.state.y)
DQNState.append(self.state.energy)
# Add position of bots
for player in self.state.players:
if player["playerId"] != self.state.id:
DQNState.append(player["posx"])
DQNState.append(player["posy"])
# Convert the DQNState from list to array for training
DQNState = np.array(DQNState)
return DQNState
def get_reward(self):
# Calculate reward
reward = 0
score_action = self.state.score - self.score_pre
self.score_pre = self.state.score
if score_action > 0:
# If the DQN agent crafts golds, then it should obtain a positive reward (equal score_action)
reward += score_action
# If the DQN agent crashs into obstacels (Tree, Trap, Swamp), then it should be punished by a negative reward
if (self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == TreeID): # Tree
reward -= TreeID
if (self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == TrapID): # Trap
reward -= TrapID
if (self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == SwampID): # Swamp
reward -= SwampID
# If out of the map, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_WENT_OUT_MAP:
reward += -10
# Run out of energy, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_OUT_OF_ENERGY:
reward += -10
# print ("reward",reward)
return reward
def check_terminate(self):
# Checking the status of the game
# it indicates the game ends or is playing
return self.state.status != State.STATUS_PLAYING
class MinerEnv:
def __init__(self, host, port):
self.socket = GameSocket(host, port)
self.state = State()
# define action space
self.INPUTNUM = 198 # The number of input values for the DQN model
self.ACTIONNUM = 6 # The number of actions output from the DQN model
# define state space
self.gameState = None
self.reward = 0
self.terminate = False
self.score_pre = self.state.score # Storing the last score for designing the reward function
self.energy_pre = self.state.energy # Storing the last energy for designing the reward function
self.viewer = None
self.steps_beyond_done = None
def start(self): # connect to server
self.socket.connect()
def end(self): # disconnect server
self.socket.close()
def send_map_info(self, request): # tell server which map to run
self.socket.send(request)
def reset(self): # start new game
# Choosing a map in the list
# mapID = np.random.randint(1, 6) # Choosing a map ID from 5 maps in Maps folder randomly
mapID = 1
posID_x = np.random.randint(MAP_MAX_X) # Choosing a initial position of the DQN agent on
# X-axes randomly
posID_y = np.random.randint(MAP_MAX_Y) # Choosing a initial position of the DQN agent on Y-axes randomly
# Creating a request for initializing a map, initial position, the initial energy, and the maximum number of steps of the DQN agent
request = ("map" + str(mapID) + "," + str(posID_x) + "," + str(posID_y) + ",50,100")
# Send the request to the game environment (GAME_SOCKET_DUMMY.py)
self.send_map_info(request)
# Initialize the game environment
try:
message = self.socket.receive() #receive game info from server
self.state.init_state(message) #init state
except Exception as e:
import traceback
traceback.print_exc()
self.gameState = self.get_state() # Get the state after resetting.
# This function (get_state()) is an example of creating a state for the DQN model
self.reward = 0 # The amount of rewards for the entire episode
self.terminate = False # The variable indicates that the episode ends
self.steps_beyond_done = None
return self.gameState
def step(self, action): # step process
self.socket.send(str(action)) # send action to server
try:
message = self.socket.receive() # receive new state from server
self.state.update_state(message) # update to local state
except Exception as e:
import traceback
traceback.print_exc()
self.gameState = self.get_state()
self.reward = self.get_reward()
done = self.check_terminate()
return self.gameState, self.reward, done, {}
# Functions are customized by client
def get_state(self):
# Building the map
view = np.zeros([self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1], dtype=int)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if self.state.mapInfo.get_obstacle(i, j) == TreeID: # Tree
view[i, j] = -20
if self.state.mapInfo.get_obstacle(i, j) == TrapID: # Trap
view[i, j] = -10
if self.state.mapInfo.get_obstacle(i, j) == SwampID: # Swamp
view[i, j] = self.state.mapInfo.get_obstacle_value(i, j)
if self.state.mapInfo.gold_amount(i, j) > 0:
view[i, j] = self.state.mapInfo.gold_amount(i, j)
# print(view)
DQNState = view.flatten().tolist() #Flattening the map matrix to a vector
# Add position and energy of agent to the DQNState
DQNState.append(self.state.x)
DQNState.append(self.state.y)
DQNState.append(self.state.energy)
me = {"playerId": 1, "energy": self.state.energy, "posx": self.state.x, "posy": self.state.y,
"lastAction": self.state.lastAction, "score": self.state.score, "status": self.state.status}
#Add position of bots
for player in self.state.players:
if player["playerId"] != self.state.id:
DQNState.append(player["posx"])
DQNState.append(player["posy"])
#Convert the DQNState from list to array for training
DQNState = np.array(DQNState)
return DQNState
def get_reward(self):
# Calculate reward
reward = 0
score_action = self.state.score - self.score_pre
energy_consume = self.energy_pre - self.state.energy
self.score_pre = self.state.score
self.energy_pre = self.state.energy
reward = score_action - 0.2 * energy_consume
# if score_action > 0:
# #If the DQN agent crafts golds, then it should obtain a positive reward (equal score_action)
# reward += score_action
#
# #If the DQN agent crashs into obstacels (Tree, Trap, Swamp), then it should be punished by a negative reward
# if self.state.mapInfo.get_obstacle(self.state.x, self.state.y) == TreeID: # Tree
# reward -= TreeID
# if self.state.mapInfo.get_obstacle(self.state.x, self.state.y) == TrapID: # Trap
# reward -= TrapID
# if self.state.mapInfo.get_obstacle(self.state.x, self.state.y) == SwampID: # Swamp
# reward -= SwampID
# If out of the map, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_WENT_OUT_MAP:
reward += -10
# Run out of energy, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_OUT_OF_ENERGY:
reward += -10
# print ("reward",reward)
return reward
def check_terminate(self):
# Checking the status of the game
# it indicates the game ends or is playing
return self.state.status != State.STATUS_PLAYING
def updateObservation(self):
return
def render(self, mode='human', close=False):
return
def close(self):
"""Override in your subclass to perform any necessary cleanup.
Environments will automatically close() themselves when
garbage collected or when the program exits.
"""
raise NotImplementedError()
def seed(self, seed=None):
"""Sets the seed for this env's random number generator(s).
# Returns
Returns the list of seeds used in this env's random number generators
"""
raise NotImplementedError()
def configure(self, *args, **kwargs):
"""Provides runtime configuration to the environment.
This configuration should consist of data that tells your
environment how to run (such as an address of a remote server,
or path to your ImageNet data). It should not affect the
semantics of the environment.
"""
raise NotImplementedError()
class MinerEnv:
def __init__(self, host, port):
self.socket = GameSocket(host, port)
self.state = State()
self.pre_x = 0
self.pre_y = 0
self.pre_energy = 0
#self.pre_action = ''
self.score_pre = self.state.score#Storing the last score for designing the reward function
def start(self): #connect to server
self.socket.connect()
def end(self): #disconnect server
self.socket.close()
def send_map_info(self, request):#tell server which map to run
self.socket.send(request)
def reset(self): #start new game
try:
message = self.socket.receive() #receive game info from server
self.state.init_state(message) #init state
except:
import traceback
traceback.print_exc()
def step(self, action): #step process
#self.pre_action = action
self.pre_energy = self.state.energy
self.pre_x, self.pre_y = self.state.x,self.state.y # store the last coordinate
self.socket.send(action) #send action to server
try:
message = self.socket.receive() #receive new state from server
self.state.update_state(message) #update to local state
# new_state = str_2_json(message)
# players = new_state["players"]
# print('length of players in step', len(players))
except:
import traceback
traceback.print_exc()
# print(self.state.players)
# Functions are customized by client
def get_state(self):
# Building the map
#print(self.state.x,self.state.y)
view = np.zeros((5*(self.state.mapInfo.max_x + 1), 5*(self.state.mapInfo.max_y + 1), 6), dtype=int)
#view[0:3, :] = -10
#view[-3:, :] = -10
#view[:, 0:3] = -10
#view[:, -3:] = -10
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if self.state.mapInfo.get_obstacle(i, j) == TreeID: # Tree # trap map
view[5*i:5*i+5, 5*j:5*j+5,0] = -TreeID
if self.state.mapInfo.get_obstacle(i, j) == TrapID: # Trap # trap map
view[5*i:5*i+5, 5*j:5*j+5,0] = -TrapID
if self.state.mapInfo.get_obstacle(i, j) == SwampID: # Swamp # trap map
view[5*i:5*i+5, 5*j:5*j+5,0] = -SwampID
if self.state.mapInfo.gold_amount(i, j) > 0:
view[5*i:5*i+5, 5*j:5*j+5,0] = self.state.mapInfo.gold_amount(i, j)/1000 ## gold map
for stt,player in enumerate(self.state.players):
if player["playerId"] != self.state.id:
try:
if player["status"] not in [1,2,3]:
try:
view[5*player["posx"]:5*player["posx"]+5,5*player["posy"]:5*player["posy"]+5,stt + 1] = player["energy"]/50
except:
view[5*player["posx"]:5*player["posx"]+5,5*player["posy"]:5*player["posy"]+5,stt + 1] = 1
except:
view[5*player["posx"]: 5*player["posx"]+5,5*player["posy"]:5*player["posy"]+5,stt]= 1
# print(self.state.players)
else:
try:
view[5*self.state.x:5*self.state.x+5,5*self.state.y:5*self.state.y+5,2]= self.state.energy
except:
print('out of map')
DQNState = np.array(view)
return DQNState
def get_reward(self,action):
# Calculate reward
reward = 0
score_action = self.state.score - self.score_pre
self.score_pre = self.state.score
pre_x, pre_y =self.pre_x,self.pre_y
if self.state.energy >=45 and self.state.lastAction == 4:
reward += -0.2
#plus a small bonus if the agent go to a coordinate that has golds
if self.state.mapInfo.gold_amount(self.state.x,self.state.y) >= 50:
reward += 0.2
#If the DQN agent crafts golds, then it should obtain a positive reward (equal score_action)
if score_action > 0:
reward += score_action/50
# if still in the map, plus a small bonus
if self.state.status == State.STATUS_PLAYING:
reward += 0.1
# if there is no gold, but the agent still crafts golds, it will be punished
if self.state.mapInfo.get_obstacle(pre_x,pre_y)<4 and int(self.state.lastAction)==5:
reward+=-0.2
if (self.state.mapInfo.gold_amount(pre_x,pre_y) >= 50 and self.pre_energy >15) and (int(self.state.lastAction)!=5):
reward+=-0.2
# If out of the map, then the DQN agent should be punished by a larger nagative reward.
#if self.state.status == State.STATUS_ELIMINATED_WENT_OUT_MAP:
# reward = -1
#Run out of energy, then the DQN agent should be punished by a larger nagative reward.
#if self.state.status == State.STATUS_ELIMINATED_OUT_OF_ENERGY:
# reward = -1
# print ("reward",reward)
#if self.state.status == State.STATUS_STOP_END_STEP:
# reward = +2
return reward
def check_terminate(self):
#Checking the status of the game
#it indicates the game ends or is playing
return self.state.status != State.STATUS_PLAYING
class MinerEnv:
def __init__(self, host, port):
self.socket = GameSocket(host, port)
self.state = State()
self.score_pre = self.state.score # Storing the last score for designing the reward function
self.energy_pre = self.state.energy
#self.x_pre = self.state.x
#self.y_pre = self.state.y
def start(self): # connect to server
self.socket.connect()
def end(self): # disconnect server
self.socket.close()
def send_map_info(self, request): # tell server which map to run
self.socket.send(request)
def reset(self): # start new game
try:
message = self.socket.receive() # receive game info from server
self.state.init_state(message) # init state
except Exception as e:
import traceback
traceback.print_exc()
def step(self, action): # step process
self.socket.send(action) # send action to server
try:
message = self.socket.receive() # receive new state from server
self.state.update_state(message) # update to local state
except Exception as e:
import traceback
traceback.print_exc()
# Functions are customized by client
def get_state(self, remain_steps, initial_flag=False):
# update pre position, score, energy
#self.x_pre = self.state.x
#self.y_pre = self.state.y
self.score_pre = self.state.score
self.energy_pre = self.state.energy
# depth = 3 # goal, min_energy, max_energy
depth = 15 # goal, min_energy, max_energy, 4 player position
goal_depth = 0
min_energy_depth = 1
max_energy_depth = 2
my_agent_depth = 3
bot1_depth = 4
bot2_depth = 5
bot3_depth = 6
goal_pos = 7
tree_pos = 8
trap_pos = 9
swamp_pos_5 = 10
swamp_pos_20 = 11
swamp_pos_40 = 12
swamp_pos_100 = 13
ground_position = 14
# len_player_infor = 6 * 4
len_player_infor = 2 + 8 + 6
# max_goal = 67 * 50 * 4 # assume 67 steps for mining and 33 steps for relaxing
max_goal = 1250
max_energy = 100
# max_x = self.state.mapInfo.max_x
# max_y = self.state.mapInfo.max_y
max_player_energy = 50
max_score = 3000
# max_score = 67 * 50
max_last_action = 6 + 1 # 1 because of None
max_status = 5
# Building the map
view_1 = np.zeros([
self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1, depth
],
dtype=float)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
# ground
view_1[i, j, min_energy_depth] = -1 / max_energy
view_1[i, j, max_energy_depth] = -1 / max_energy
view_1[i, j, ground_position] = 1
goal = self.state.mapInfo.gold_amount(i, j)
if goal > 0:
view_1[i, j, ground_position] = 0
view_1[i, j, goal_pos] = 1
view_1[i, j, min_energy_depth] = -4 / max_energy
view_1[i, j, max_energy_depth] = -4 / max_energy
view_1[i, j, goal_depth] = goal / max_goal
for obstacle in self.state.mapInfo.obstacles:
i = obstacle["posx"]
j = obstacle["posy"]
if obstacle["type"] == TreeID: # Tree
view_1[i, j, ground_position] = 0
view_1[i, j, tree_pos] = 1
view_1[i, j, min_energy_depth] = -5 / max_energy # -5 ~ -20
view_1[i, j, max_energy_depth] = -20 / max_energy # -5 ~ -20
elif obstacle["type"] == TrapID: # Trap
if obstacle["value"] != 0:
view_1[i, j, ground_position] = 0
view_1[i, j, trap_pos] = 1
view_1[i, j, min_energy_depth] = obstacle["value"] / max_energy
view_1[i, j, max_energy_depth] = obstacle["value"] / max_energy
elif obstacle["type"] == SwampID: # Swamp
view_1[i, j, ground_position] = 0
view_1[i, j, min_energy_depth] = obstacle[
"value"] / max_energy # -5, -20, -40, -100
view_1[i, j, max_energy_depth] = obstacle[
"value"] / max_energy # -5, -20, -40, -100
if obstacle["value"] == -5:
view_1[i, j, swamp_pos_5] = 1
elif obstacle["value"] == -20:
view_1[i, j, swamp_pos_20] = 1
elif obstacle["value"] == -40:
view_1[i, j, swamp_pos_40] = 1
elif obstacle["value"] == -100:
view_1[i, j, swamp_pos_100] = 1
"""
for goal in self.state.mapInfo.golds:
i = goal["posx"]
j = goal["posy"]
view_1[i, j, min_energy_depth] = 4 / max_energy
view_1[i, j, max_energy_depth] = 4 / max_energy
view_1[i, j, goal_depth] = goal["amount"] / max_goal
"""
# Add player's information
view_2 = np.zeros([len_player_infor * 4 + 1],
dtype=float) # +1 remaining steps
index_player = 0
if (0 <= self.state.x <= self.state.mapInfo.max_x) and \
(0 <= self.state.y <= self.state.mapInfo.max_y):
view_1[self.state.x, self.state.y, my_agent_depth] = 1
view_2[index_player * len_player_infor +
0] = self.state.energy / max_player_energy
view_2[index_player * len_player_infor +
1] = self.state.score / max_score
if self.state.lastAction is None: # 0 step
view_2[index_player * len_player_infor + 2 +
max_last_action] = 1
else: # > 1 step
view_2[index_player * len_player_infor + 2 +
self.state.lastAction] = 1
view_2[index_player * len_player_infor + 2 + max_last_action + 1 +
self.state.status] = 1
bot_depth = my_agent_depth
for player in self.state.players:
if player["playerId"] != self.state.id:
index_player += 1
bot_depth += 1
if (0 <= player["posx"] <= self.state.mapInfo.max_x) and \
(0 <= player["posy"] <= self.state.mapInfo.max_y):
if "energy" in player: # > 1 step
if player["status"] == self.state.STATUS_PLAYING:
view_1[player["posx"], player["posy"],
bot_depth] = 1
view_2[index_player * len_player_infor +
0] = player["energy"] / max_player_energy
view_2[index_player * len_player_infor +
1] = player["score"] / max_score
view_2[index_player * len_player_infor + 2 +
player["lastAction"]] = 1 # one hot
view_2[index_player * len_player_infor + 2 +
max_last_action + 1 + player["status"]] = 1
elif initial_flag: # 0 step, initial state
view_1[player["posx"], player["posy"], bot_depth] = 1
view_2[index_player * len_player_infor +
0] = 50 / max_player_energy
view_2[index_player * len_player_infor +
1] = 0 / max_score
view_2[index_player * len_player_infor + 2 +
max_last_action] = 1 # one hot
view_2[index_player * len_player_infor + 2 +
max_last_action + 1 +
self.state.STATUS_PLAYING] = 1
view_2[-1] = remain_steps / 100
# Convert the DQNState from list to array for training
DQNState_map = np.array(view_1)
DQNState_users = np.array(view_2)
return DQNState_map, DQNState_users
def get_reward(self, num_of_wrong_relax, num_of_wrong_mining):
# return -0.01 ~ 0.01
# reward must target to mine goal
max_reward = 50
reward_died = -50 # ~ double max reward
# reward_died = -25 # let a try
reward_enter_goal = max_reward / 20 # 5
# Calculate reward
reward = 0 # moving, because agent will die at the max step
energy_action = self.state.energy - self.energy_pre # < 0 if not relax
score_action = self.state.score - self.score_pre # >= 0
if score_action > 0:
reward = score_action / 2500 # max ~2500 / episode
else:
# moving
#if int(self.state.lastAction) < 4:
# # enter gold
# if self.state.mapInfo.gold_amount(self.state.x, self.state.y) > 0:
# reward = reward_enter_goal / 2500
# mining but cannot get gold
if (int(self.state.lastAction) == 5) and (score_action == 0):
# reward = reward_died / 10 / max_reward
num_of_wrong_mining += 1
# relax when energy > 40 or cannot get more energy
elif int(self.state.lastAction) == 4:
if self.energy_pre > 40 or energy_action == 0:
# reward = reward_died / 10 / max_reward
num_of_wrong_relax += 1
# at gold but move to ground
# if (int(self.state.lastAction) < 4) and (self.state.mapInfo.gold_amount(self.x_pre, self.y_pre) > 0) \
# and (self.state.mapInfo.gold_amount(self.state.x, self.state.y) == 0):
# reward = reward_died
# relax when energy > 40
#elif self.energy_pre > 40 and int(self.state.lastAction) == 4:
# reward = reward_died / 4
# relax but cannot get more energy
#elif int(self.state.lastAction) == 4 and energy_action == 0:
# reward = reward_died / 4
# If out of the map, then the DQN agent should be punished by a larger negative reward.
#if self.state.status == State.STATUS_ELIMINATED_WENT_OUT_MAP or self.state.status == State.STATUS_ELIMINATED_INVALID_ACTION:
# reward = reward_died / max_reward
#elif self.state.status == State.STATUS_ELIMINATED_OUT_OF_ENERGY or self.state.status == State.STATUS_STOP_EMPTY_GOLD \
# or self.state.status == State.STATUS_STOP_END_STEP:
if self.state.status != State.STATUS_PLAYING:
if self.state.score == 0:
reward = reward_died / max_reward # -1
if self.state.status == State.STATUS_ELIMINATED_WENT_OUT_MAP or self.state.status == State.STATUS_ELIMINATED_OUT_OF_ENERGY:
reward = reward_died / max_reward # -1
# print ("reward",reward)
#return reward / max_reward / self.state.mapInfo.maxStep # 100 steps
return reward, num_of_wrong_relax, num_of_wrong_mining
def check_terminate(self):
# Checking the status of the game
# it indicates the game ends or is playing
return self.state.status != State.STATUS_PLAYING
class MinerEnv:
def __init__(self, host, port):
self.socket = GameSocket(host, port)
self.state = State()
self.score_pre = self.state.score # Storing the last score for designing the reward function
def start(self): # connect to server
self.socket.connect()
def end(self): # disconnect server
self.socket.close()
def send_map_info(self, request): # tell server which map to run
self.socket.send(request)
def reset(self): # start new game
try:
message = self.socket.receive() # receive game info from server
self.state.init_state(message) # init state
except Exception as e:
import traceback
traceback.print_exc()
def step(self, action): # step process
self.socket.send(action) # send action to server
try:
message = self.socket.receive() # receive new state from server
self.state.update_state(message) # update to local state
except Exception as e:
import traceback
traceback.print_exc()
# Functions are customized by client
def get_state(self):
# Building the map
view = np.zeros(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1],
dtype=int)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if self.state.mapInfo.get_obstacle(i, j) == TreeID: # Tree
view[i, j] = -TreeID
if self.state.mapInfo.get_obstacle(i, j) == TrapID: # Trap
view[i, j] = -TrapID
if self.state.mapInfo.get_obstacle(i, j) == SwampID: # Swamp
view[i, j] = -SwampID
if self.state.mapInfo.gold_amount(i, j) > 0:
view[i, j] = self.state.mapInfo.gold_amount(i, j)
DQNState = view.flatten().tolist(
) # Flattening the map matrix to a vector
# Add position and energy of agent to the DQNState
next_round_energy = self.get_next_round_engergy()
DQNState.append(next_round_energy)
DQNState.append(self.state.x)
DQNState.append(self.state.y)
DQNState.append(self.state.score)
DQNState.append(self.state.energy)
# DQNState.append(self.state)
# Add position of bots
for player in self.state.players:
if player["playerId"] != self.state.id:
DQNState.append(player["posx"])
DQNState.append(player["posy"])
energy = 0
score = 0
free_count = 0
if 'energy' in player:
energy = player["energy"]
if 'score' in player:
score = player["score"]
if 'free_count' in player:
free_count = player["free_count"]
DQNState.append(energy)
DQNState.append(score)
DQNState.append(free_count)
# Convert the DQNState from list to array for training
DQNState = np.array(DQNState)
return DQNState
def get_next_round_engergy(self):
free_count = 0
for p in self.state.players:
if p['playerId'] == self.state.id:
free_count = p['freeCount']
next_e = self.state.energy
for i in range(4 - free_count):
next_e += next_e / max(i, 1)
return next_e
def dig_score(self):
pass
def get_reward(self):
# Calculate reward
reward = 0
score_action = self.state.score # - self.score_pre
self.score_pre = self.state.score
if score_action > 0:
# If the DQN agent crafts golds, then it should obtain a positive reward (equal score_action)
reward += score_action
# print('Craft gold : {}'.format(score_action))
next_e = self.get_next_round_engergy()
if next_e <= 0: # Do not stand while you have full energy :(
reward -= 100
if next_e >= 50 and self.state.lastAction == 4:
reward -= 100
# If the DQN agent crashs into obstacels (Tree, Trap, Swamp), then it should be punished by a negative reward
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == TreeID: # Tree
reward -= TreeID
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == TrapID: # Trap
reward -= TrapID
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == SwampID: # Swamp
reward -= SwampID
# If out of the map, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_WENT_OUT_MAP:
reward += -100
# Run out of energy, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_OUT_OF_ENERGY:
reward += -100
return reward / 100.
def check_terminate(self):
# Checking the status of the game
# it indicates the game ends or is playing
return self.state.status != State.STATUS_PLAYING
class MinerEnv:
def __init__(self, host, port):
self.socket = GameSocket(host, port)
self.state = State()
self.score_pre = self.state.score #Storing the last score for designing the reward function
self.decay = 27
self.area_affect = 3
self.affect_eff = 0.92
self.view = None
self.energy_view = None
self.current_action = None
self.gold_map = None
self.gold_map_origin = None
def start(self): #connect to server
self.socket.connect()
def end(self): #disconnect server
self.socket.close()
def send_map_info(self, request): #tell server which map to run
self.socket.send(request)
def reset(self): #start new game
# Choosing a map in the list
# mapID = np.random.randint(1, 6) # Choosing a map ID from 5 maps in Maps folder randomly
mapID = 1
posID_x = np.random.randint(
MAP_MAX_X) # Choosing a initial position of the DQN agent on
# posID_x = 12
# X-axes randomly
posID_y = np.random.randint(
MAP_MAX_Y
) # Choosing a initial position of the DQN agent on Y-axes randomly
# posID_y = 1
# Creating a request for initializing a map, initial position, the initial energy, and the maximum number of steps of the DQN agent
request = ("map" + str(mapID) + "," + str(posID_x) + "," +
str(posID_y) + ",50,100")
# Send the request to the game environment (GAME_SOCKET_DUMMY.py)
self.send_map_info(request)
try:
message = self.socket.receive() #receive game info from server
print(message)
self.state.init_state(message) #init state
print(self.state.score)
except Exception as e:
import traceback
traceback.print_exc()
def step(self, action): #step process
self.socket.send(action) #send action to server
try:
message = self.socket.receive() #receive new state from server
#print("New state: ", message)
self.state.update_state(message) #update to local state
print(self.state.score)
except Exception as e:
import traceback
traceback.print_exc()
# Functions are customized by client
def get_state(self):
# Building the map
self.view = np.zeros(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1],
dtype=int)
self.energy_view = np.zeros(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1],
dtype=int)
self.gold_map = np.zeros(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1],
dtype=int)
gold_opt = np.zeros(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1],
dtype=int)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if self.state.mapInfo.get_obstacle(i, j) == TreeID: # Tree
self.view[i, j] = -20
self.energy_view[i, j] = -20
elif self.state.mapInfo.get_obstacle(i, j) == TrapID: # Trap
self.view[i, j] = -10
self.energy_view[i, j] = -10
elif self.state.mapInfo.get_obstacle(i, j) == SwampID: # Swamp
self.view[i,
j] = self.state.mapInfo.get_obstacle_value(i, j)
self.energy_view[
i, j] = self.state.mapInfo.get_obstacle_value(i, j)
elif self.state.mapInfo.gold_amount(i, j) > 0:
self.view[i, j] = self.state.mapInfo.gold_amount(i, j)
self.energy_view[i, j] = -4
self.gold_map[i, j] = self.state.mapInfo.gold_amount(i, j)
else:
self.view[i, j] = -1
self.energy_view[i, j] = -1
self.gold_map_origin = copy.deepcopy(self.gold_map)
# print(self.gold_map)
# player update goldmap
for player in self.state.players:
if player["playerId"] != self.state.id:
x = player["posx"]
y = player["posy"]
if 0 <= x <= self.state.mapInfo.max_x and 0 <= y <= self.state.mapInfo.max_y:
if self.gold_map[x][y] > 0:
if x != self.state.x or y != self.state.y:
self.gold_map[x][y] = self.gold_map[x][y] * 0.63
self.view[x][y] = self.gold_map[x][y]
else:
for t in range(1, self.area_affect + 1):
for k in range(-t, t):
if 0 <= x + k <= self.state.mapInfo.max_x and 0 <= y + t - abs(
k) <= self.state.mapInfo.max_y:
if self.gold_map[x + k][y + t -
abs(k)] > 0:
self.gold_map[x + k][
y + t - abs(k)] = self.gold_map[
x + k][y + t - abs(k)] * pow(
self.affect_eff,
self.area_affect + 1 - t)
self.view[x +
k][y + t -
abs(k)] = self.gold_map[
x + k][y + t - abs(k)]
if 0 <= x - k <= self.state.mapInfo.max_x and 0 <= y - t + abs(
k) <= self.state.mapInfo.max_y:
if self.gold_map[x - k][y - t +
abs(k)] > 0:
self.gold_map[x - k][
y - t + abs(k)] = self.gold_map[
x - k][y - t + abs(k)] * pow(
self.affect_eff,
self.area_affect + 1 - t)
self.view[x -
k][y - t +
abs(k)] = self.gold_map[
x - k][y - t + abs(k)]
print(self.gold_map)
arr = []
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if self.state.mapInfo.gold_amount(i, j) > 0:
gold_est = np.zeros([
self.state.mapInfo.max_x + 1,
self.state.mapInfo.max_y + 1
],
dtype=int)
gold_est[i][j] = self.gold_map[i][j]
for a in range(0, i):
gold_est[i - a - 1][j] = max(
gold_est[i - a][j] - self.decay +
self.view[i - a - 1][j], 0)
for b in range(i + 1, self.state.mapInfo.max_x + 1):
gold_est[b][j] = max(
gold_est[b - 1][j] - self.decay + self.view[b][j],
0)
for c in range(0, j):
gold_est[i][j - c - 1] = max(
gold_est[i][j - c] - self.decay +
self.view[i][j - c - 1], 0)
for d in range(j + 1, self.state.mapInfo.max_y + 1):
gold_est[i][d] = max(
gold_est[i][d - 1] - self.decay + self.view[i][d],
0)
for x in range(0, i):
for y in range(0, j):
gold_est[i - x - 1][j - y - 1] = max(gold_est[i - x][j - y - 1],
gold_est[i - x - 1][j - y]) - self.decay + \
self.view[i - x - 1][j - y - 1]
for x in range(0, i):
for y in range(j + 1, self.state.mapInfo.max_y + 1):
gold_est[i - x - 1][y] = max(gold_est[i - x][y], gold_est[i - x - 1][y - 1]) - self.decay + \
self.view[i - x - 1][y]
for x in range(i + 1, self.state.mapInfo.max_x + 1):
for y in range(0, j):
gold_est[x][j - y - 1] = max(gold_est[x][j - y], gold_est[x - 1][j - y - 1]) - self.decay + \
self.view[x][j - y - 1]
for x in range(i + 1, self.state.mapInfo.max_x + 1):
for y in range(j + 1, self.state.mapInfo.max_y + 1):
gold_est[x][y] = max(
gold_est[x - 1][y],
gold_est[x][y -
1]) - self.decay + self.view[x][y]
# print(i, j, self.state.mapInfo.gold_amount(i, j))
# print(gold_est)
arr.append(gold_est)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
for t in range(len(arr)):
if gold_opt[i][j] < arr[t][i][j]:
gold_opt[i][j] = arr[t][i][j]
# print(gold_opt)
return np.array(gold_opt)
def check_terminate(self):
return self.state.status != State.STATUS_PLAYING
class MinerEnv:
def __init__(self, host, port):
self.socket = GameSocket(host, port)
self.state = State()
self.score_pre = self.state.score #Storing the last score for designing the reward function
def start(self): #connect to server
self.socket.connect()
def end(self): #disconnect server
self.socket.close()
def send_map_info(self, request): #tell server which map to run
self.socket.send(request)
def reset(self): #start new game
self.state_x_pre = self.state.x
self.state_y_pre = self.state.y
self.last3position = []
self.Swamp_position = []
self.craft_no_gold = 0
self.in_gold = 0
self.premindist = 1000
#self.dmax,_,_ = self.distance_value_trade_off()
try:
message = self.socket.receive() #receive game info from server
self.state.init_state(message) #init state
except Exception as e:
import traceback
traceback.print_exc()
def step(self, action): #step process
self.state_x_pre = self.state.x
self.state_y_pre = self.state.y
self.last3position.append([self.state.x, self.state.y])
if len(self.last3position) > 3:
self.last3position.pop(0)
#print(self.last3position)
self.socket.send(action) #send action to server
try:
message = self.socket.receive() #receive new state from server
self.state.update_state(message) #update to local state
except Exception as e:
import traceback
traceback.print_exc()
# Functions are customized by client
def get_reward(self):
# Calculate reward
reward = 0
score_action = self.state.score - self.score_pre
self.score_pre = self.state.score
# dmax, xgold, ygold = self.distance_value_trade_off()
# print("come to: ", dmax, xgold, ygold, self.state.mapInfo.gold_amount(xgold, ygold))
# if dmax >= self.dmax:
# reward += 0.1
# print(self.dmax, self.state.x, self.state.y, self.state.mapInfo.gold_amount(self.state.x, self.state.y))
# self.dmax = dmax
if score_action > 0:
#If the DQN agent crafts golds, then it should obtain a positive reward (equal score_action)
reward += score_action / 50 * 10
#If the DQN agent crashs into obstacels (Tree, Trap, Swamp), then it should be punished by a negative reward
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == TreeID: # Tree
reward -= 0.06 * 3
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == TrapID: # Trap
reward -= 0.03 * 3
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == SwampID: # Swamp
if [self.state.x,
self.state.y] in self.Swamp_position: # go to Swamp again
reward -= 0.5
else:
reward -= 0.05 # first time go to swamp
#reward -=0.4
if self.state.mapInfo.gold_amount(self.state.x, self.state.y) >= 50:
reward += 0.3
if self.state.mapInfo.gold_amount(
self.state_x_pre, self.state_y_pre
) >= 50 and self.state.lastAction != 5: # in gold but don't craft
self.in_gold += 1
reward -= 0.5
if self.state.lastAction == 5 and score_action == 0: # not in gold but craft
self.craft_no_gold += 1
reward -= 0.5
# If out of the map, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_WENT_OUT_MAP:
reward -= 20
#Run out of energy, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_OUT_OF_ENERGY:
reward -= 0.7
if self.state.status == State.STATUS_PLAYING:
reward += 0.1
# print ("reward",reward)
return reward
def get_reward_complex(self):
reward = 0
score_action = self.state.score - self.score_pre
self.score_pre = self.state.score
### reward for gold
golds = self.state.mapInfo.golds
miner_posx, miner_posy = self.state.x, self.state.y
target_x, target_y = miner_posx, miner_posy
mindist = 1000
for gold in golds:
dist = distance([gold["posx"], gold["posy"]],
[miner_posx, miner_posy]) - self.reward_gold(
[gold["posx"], gold["posy"]])
if dist < mindist:
mindist = dist
if mindist < self.premindist:
reward += 0.5
self.premindist = mindist
####
if score_action > 0:
#If the DQN agent crafts golds, then it should obtain a positive reward (equal score_action)
reward += score_action / 50 * 10
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == TreeID: # Tree
reward -= 0.06 * 3
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == TrapID: # Trap
reward -= 0.03 * 3
if self.state.mapInfo.get_obstacle(self.state.x,
self.state.y) == SwampID: # Swamp
if [self.state.x,
self.state.y] in self.Swamp_position: # go to Swamp again
reward -= 0.5
else:
reward -= 0.05 # first time go to swamp
#reward -=0.4
if self.state.mapInfo.gold_amount(
self.state_x_pre, self.state_y_pre
) >= 50 and self.state.lastAction != 5: # in gold but don't craft
reward -= 0.5
if self.state.lastAction == 5 and score_action == 0: # not in gold but craft
reward -= 0.5
if self.state.energy >= 45 and self.state.lastAction == 4:
reward -= 1
# If out of the map, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_WENT_OUT_MAP:
reward -= 20
#Run out of energy, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_OUT_OF_ENERGY:
reward -= 0.7
if self.state.status == State.STATUS_PLAYING:
reward += 0.1
return reward
def reward_gold(self, gold_pos):
x, y = gold_pos[0], gold_pos[1]
reward = 0
for stt, (i, j) in enumerate(
zip([-1, 1, 0, 0, -1, 1, -1, 1], [0, 0, -1, 1, -1, -1, 1, 1])):
xnew, ynew = x + i, y + j
if xnew <= self.state.mapInfo.max_x and xnew >=0 \
and ynew <= self.state.mapInfo.max_y and ynew >= 0:
amount = self.state.mapInfo.gold_amount(xnew, ynew)
if amount >= 100 and amount <= 200:
reward += 1
if amount > 200 and amount <= 500:
reward += 2
if amount > 500:
reward += 3
if amount >= 1000:
reward += 5
return reward
def get_state_tensor(self, scale_map):
n = scale_map
view = torch.zeros((7, n * (self.state.mapInfo.max_x + 1), n *
(self.state.mapInfo.max_y + 1)),
dtype=torch.float)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if self.state.mapInfo.get_obstacle(
i, j) == TreeID: # Tree # trap map
view[2, n * i:n * i + n, n * j:n * j + n] = -TreeID
view[0, n * i:n * i + n, n * j:n * j + n] = -TreeID
if self.state.mapInfo.get_obstacle(
i, j) == TrapID: # Trap # trap map
view[2, n * i:n * i + n, n * j:n * j + n] = -TrapID
view[0, n * i:n * i + n, n * j:n * j + n] = -TrapID
if self.state.mapInfo.get_obstacle(
i, j) == SwampID: # Swamp # trap map
if [i, j] not in self.Swamp_position:
view[2, n * i:n * i + n,
n * j:n * j + n] = -SwampID # -3
view[0, n * i:n * i + n, n * j:n * j + n] = -SwampID
else:
view[2, n * i:n * i + n,
n * j:n * j + n] = -SwampID - 3 # -6
view[0, n * i:n * i + n,
n * j:n * j + n] = -SwampID - 3
gold_ = self.state.mapInfo.gold_amount(i, j)
if gold_ > 0:
view[1, n * i:n * i + n,
n * j:n * j + n] = gold_ / 1000 ##/10 gold map
view[0, n * i:n * i + n, n * j:n * j + n] = gold_ / 1000
index = 3
playerid_list = []
for stt, player in enumerate(self.state.players):
playerid_list.append(player["playerId"])
if player["playerId"] != self.state.id:
try:
if player["status"] not in [1, 2, 3]:
try:
view[index + 1,
n * player["posx"]:n * player["posx"] + n,
n * player["posy"]:n * player["posy"] +
n] = player["energy"] / 50
except:
view[index + 1,
n * player["posx"]:n * player["posx"] + n,
n * player["posy"]:n * player["posy"] + n] = 1
index += 1
except:
view[index + 1, n * player["posx"]:n * player["posx"] + n,
n * player["posy"]:n * player["posy"] + n] = 1
# print(self.state.players)
#print(view[player["posx"]: player["posx"]+1, player["posy"]: player["posy"]+1, stt])
#print(np.unique(a-view[:,:,stt]))
index += 1
else:
try:
view[3, n * self.state.x:n * self.state.x + n,
n * self.state.y:n * self.state.y +
n] = self.state.energy / 50
except:
print('out of map')
if self.state.id not in playerid_list:
view[3, n * self.state.x:n * self.state.x + n, n *
self.state.y:n * self.state.y + n] = self.state.energy / 50
#print("check: ", np.unique(view[3,:,:]))
DQNState = view
return DQNState
def get_state2(self, limit):
# Building the map
view = np.zeros([limit * 2 + 1, limit * 2 + 1], dtype=int)
max_x, max_y = self.state.mapInfo.max_x, self.state.mapInfo.max_y
xlimit_below = np.clip(self.state.x - limit, 0, max_x) - np.clip(
self.state.x + limit - max_x, 0, limit)
xlimit_up = np.clip(self.state.x + limit, 0, max_x) + np.clip(
0 - self.state.x + limit, 0, limit)
ylimit_below = np.clip(self.state.y - limit, 0, max_y) - np.clip(
self.state.y + limit - max_y, 0, limit)
ylimit_up = np.clip(self.state.y + limit, 0, max_y) + np.clip(
0 - self.state.y + limit, 0, limit)
#print(xlimit_below, xlimit_up, ylimit_below, ylimit_up, self.state.x, self.state.y)
for i in range(xlimit_below, xlimit_up + 1):
for j in range(ylimit_below, ylimit_up + 1):
if self.state.mapInfo.get_obstacle(i, j) == TreeID: # Tree
view[i - xlimit_below, j - ylimit_below] = -TreeID
if self.state.mapInfo.get_obstacle(i, j) == TrapID: # Trap
view[i - xlimit_below, j - ylimit_below] = -TrapID
if self.state.mapInfo.get_obstacle(i, j) == SwampID: # Swamp
view[i - xlimit_below, j - ylimit_below] = -SwampID
if self.state.mapInfo.gold_amount(i, j) > 0:
view[i - xlimit_below,
j - ylimit_below] = self.state.mapInfo.gold_amount(
i, j) / 10
DQNState = view.flatten().tolist(
) #Flattening the map matrix to a vector
# Add position and energy of agent to the DQNState
DQNState.append(self.state.x - xlimit_below)
DQNState.append(self.state.y - ylimit_below)
DQNState.append(self.state.energy)
#Add position of bots
# for player in self.state.players:
# if player["playerId"] != self.state.id:
# DQNState.append(player["posx"])
# DQNState.append(player["posy"])
#Convert the DQNState from list to array for training
DQNState = np.array(DQNState)
return DQNState
def update_swamp(self):
for player in self.state.players:
if self.state.mapInfo.get_obstacle(
player["posx"], player["posy"]) == 3 and [
player["posx"], player["posy"]
] not in self.Swamp_position:
self.Swamp_position.append([player["posx"], player["posy"]])
def check_terminate(self):
#Checking the status of the game
#it indicates the game ends or is playing
return self.state.status != State.STATUS_PLAYING
class MinerEnv:
def __init__(self, host, port):
self.socket = GameSocket(host, port)
self.state = State()
# define action space
# self.INPUT_DIM = (21, 9, 2) # The number of input values for the DQN model
self.INPUT_DIM = (21, 9)
self.ACTIONNUM = 6 # The number of actions output from the DQN model
# define state space
self.gameState = None
self.reward = 0
self.terminate = False
self.gold_map = None
self.dist_gold = None
self.score_pre = self.state.score # Storing the last score for designing the reward function
self.energy_pre = self.state.energy # Storing the last energy for designing the reward function
self.viewer = None
self.steps_beyond_done = None
def start(self): # connect to server
self.socket.connect()
def end(self): # disconnect server
self.socket.close()
def send_map_info(self, request): # tell server which map to run
self.socket.send(request)
def reset(self): # start new game
# Choosing a map in the list
# mapID = np.random.randint(1, 6) # Choosing a map ID from 5 maps in Maps folder randomly
mapID = 1
posID_x = np.random.randint(
MAP_MAX_X) # Choosing a initial position of the DQN agent on
# X-axes randomly
posID_y = np.random.randint(
MAP_MAX_Y
) # Choosing a initial position of the DQN agent on Y-axes randomly
# Creating a request for initializing a map, initial position, the initial energy, and the maximum number of steps of the DQN agent
request = ("map" + str(mapID) + "," + str(posID_x) + "," +
str(posID_y) + ",50,100")
# Send the request to the game environment (GAME_SOCKET_DUMMY.py)
self.send_map_info(request)
# Initialize the game environment
try:
message = self.socket.receive() #receive game info from server
self.state.init_state(message) #init state
except Exception as e:
import traceback
traceback.print_exc()
self.gameState = self.get_state() # Get the state after resetting.
# This function (get_state()) is an example of creating a state for the DQN model
distance = 500
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if self.gold_map[i][j] > 0:
distance_temp = abs(self.state.x - i) + abs(
self.state.y - j) #- self.gold_map[i][j]
if distance > distance_temp:
distance = distance_temp
self.dist_gold = distance
self.score_pre = self.state.score # Storing the last score for designing the reward function
self.energy_pre = self.state.energy # Storing the last energy for designing the reward function
self.reward = 0 # The amount of rewards for the entire episode
self.terminate = False # The variable indicates that the episode ends
self.steps_beyond_done = None
return self.gameState
def step(self, action): # step process
self.socket.send(str(action)) # send action to server
try:
message = self.socket.receive() # receive new state from server
self.state.update_state(message) # update to local state
except Exception as e:
import traceback
traceback.print_exc()
self.gameState = self.get_state()
self.reward = self.get_reward()
done = self.check_terminate()
return self.gameState, self.reward, done, {}
# Functions are customized by client
def get_state(self):
# Building the map
view = np.zeros(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1, 2],
dtype="float32")
self.gold_map = np.zeros(
[self.state.mapInfo.max_x + 1, self.state.mapInfo.max_y + 1],
dtype=int)
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if self.state.mapInfo.get_obstacle(i, j) == TreeID: # Tree
view[i, j, 0] = -20 * 1.0 / 20
# view[i, j, 0] = -TreeID
elif self.state.mapInfo.get_obstacle(i, j) == TrapID: # Trap
view[i, j, 0] = -10 * 1.0 / 20
# view[i, j, 0] = -TrapID
elif self.state.mapInfo.get_obstacle(i, j) == SwampID: # Swamp
view[i, j, 0] = self.state.mapInfo.get_obstacle_value(
i, j) * 1.0 / 20
# view[i, j, 0] = -SwampID
elif self.state.mapInfo.gold_amount(i, j) > 0:
view[i, j,
0] = self.state.mapInfo.gold_amount(i, j) * 1.0 / 100
self.gold_map[i, j] = self.state.mapInfo.gold_amount(
i, j) / 50
if self.state.status == 0:
view[self.state.x, self.state.y, 1] = self.state.energy * 1.0 / 10
# for player in self.state.players:
# if player["playerId"] != self.state.id: # view[player["posx"], player["posy"], 1] -= 1
#Convert the DQNState from list to array for training
DQNState = np.array(view)
return DQNState
def get_reward(self):
# Calculate reward
a = self.state.lastAction
e = self.state.energy
e_pre = self.energy_pre
score_action = self.state.score - self.score_pre
energy_consume = self.energy_pre - self.state.energy
if energy_consume == 0:
energy_consume = 20
elif energy_consume < 0:
energy_consume = energy_consume * 2
self.score_pre = self.state.score
self.energy_pre = self.state.energy
# calculate distance to gold
distance = 500
for i in range(self.state.mapInfo.max_x + 1):
for j in range(self.state.mapInfo.max_y + 1):
if self.gold_map[i][j] > 0:
distance_temp = abs(self.state.x - i) + abs(
self.state.y - j) # - self.gold_map[i][j]
if distance > distance_temp:
distance = distance_temp
move_distance = (self.dist_gold - distance)
if move_distance > 0:
score_distance = move_distance * 20
elif move_distance < 0:
score_distance = move_distance * 10
else:
score_distance = 0
self.dist_gold = distance
reward = score_action * 2 + score_distance - energy_consume
# If out of the map, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_WENT_OUT_MAP:
reward += -50
# Run out of energy, then the DQN agent should be punished by a larger nagative reward.
if self.state.status == State.STATUS_ELIMINATED_OUT_OF_ENERGY:
reward += -50
# print ("reward",reward)
return reward * 0.01
def check_terminate(self):
# Checking the status of the game
# it indicates the game ends or is playing
return self.state.status != State.STATUS_PLAYING
def updateObservation(self):
return
def render(self, mode='human', close=False):
return
def close(self):
"""Override in your subclass to perform any necessary cleanup.
Environments will automatically close() themselves when
garbage collected or when the program exits.
"""
raise NotImplementedError()
def seed(self, seed=None):
"""Sets the seed for this env's random number generator(s).
# Returns
Returns the list of seeds used in this env's random number generators
"""
raise NotImplementedError()
def configure(self, *args, **kwargs):
"""Provides runtime configuration to the environment.
This configuration should consist of data that tells your
environment how to run (such as an address of a remote server,
or path to your ImageNet data). It should not affect the
semantics of the environment.
"""
raise NotImplementedError()