#goal will always be upper right
goal_x = GRID_NUM - 1
goal_y = GRID_NUM - 1
#this is just so the red box is never in the same location as the black box
while True:
neg_x = np.random.randint(1, GRID_NUM - 1)
neg_y = np.random.randint(1, GRID_NUM - 1)
if ((neg_x is not black_hole_loc_y) and (neg_y is not black_hole_loc_y)):
break
num_actions = list(range(4))
agent = rl_agent(
list(range(4)), #UP,DOWN,LEFT,RIGHT
GAMMA, #gamma
ALPHA,
EPSILON)
#agent.init_q_table()
model = agent.init_NN(num_outputs=num_actions, size=GRID_NUM * GRID_NUM)
#drawScene(black_hole_loc_x, black_hole_loc_y,ball_x,ball_y,goal_x,goal_y, neg_x,neg_y)
#initalize state matrix
state = getState(black_hole_loc_x, black_hole_loc_y, ball_x, ball_y, goal_x,
goal_y, neg_x, neg_y)
start = time.time()
total_time = time.time()
episodes = 0
steps = 0
if int(action) == 1:
robot_pos = robot_pos - 1
elif int(action) == 2:
robot_pos = robot_pos + 1
return robot_pos
#RL constants
EPSILON = 1 # greedy police
ALPHA = 0.5 # learning rate
GAMMA = 1 # discount
agent = rl_agent(
list(range(3)), #STAY,LEFT,RIGHT
GAMMA,
ALPHA,
EPSILON)
agent.init_q_table()
#resetRobot()
i = 0
epochs = 10000
grid_dim = (5, 8)
robot_pos = 0
while (True):
#while(i < epochs):
def get_reward(robot_x, robot_y, ball_x,thrust ):
'''
return DISTANCEFORMULA
'''
return -1
#RL constants
EPSILON = 1 # greedy police
ALPHA = 0.5 # learning rate
GAMMA = 1 # discount
agent = rl_agent(
list(range(5)), #STAY,LEFT,RIGHT, UP, DOWN
GAMMA,
ALPHA,
EPSILON
)
robot_spaces_x = 5 #grids
robot_spaces_y = 3 #grids
gantry_plate_size = 2.6771654 #inches
robot = virtual_robot(
WIDTH = 1680,#width of monitor
HEIGHT = 1050, #height of monitor
dpi = 99, #dpi of the monitor
roi_x = 12, #inches, this is how wide in x you want the robot range to be, the black part of the virtual robot inches
roi_y = 8, #inches
number_of_states_x = robot_spaces_x, #0,1,2,3,4
number_of_states_y = robot_spaces_y, #0,1,2
initial_x = 2, #middle x
if TYPE == "x" or TYPE == "y":
num_actions = 3
num_states = 2
else:
num_actions = 5
num_states = 4
#RL constants
EPSILON = 1 # greedy police
ALPHA = 0.5 # learning rate
GAMMA = 1 # discount
agent = rl_agent(
list(range(num_actions)), #STAY,LEFT,RIGHT
GAMMA,
ALPHA,
EPSILON)
agent.init_q_table()
catch_count = 0
total_reward = 0
last_catch = 0
last_reward = 0
total_rewards = []
num_epochs = []
average_epochs = []
total_loss = []
total_catches = []
average_catches = []