# Create a random enviroment sampled from a GP with an RBF kernel and specified hyperparameters, mean function 0
# The enviorment will be constrained by a set of uniformly distributed sample points of size NUM_PTS x NUM_PTS
ranges = (0., 10., 0., 10.)
world = envlib.Environment(ranges = ranges,
NUM_PTS = 20,
variance = 100.0,
lengthscale = 1.0,
visualize = True,
seed = seed,
MIN_COLOR=MIN_COLOR,
MAX_COLOR=MAX_COLOR)
# Create the evaluation class used to quantify the simulation metrics
evaluation = evalib.Evaluation(world = world, reward_function = reward_function)
# Populate a world with obstacles
# ow = obslib.FreeWorld()
# ow = obslib.BlockWorld(ranges, 1, dim_blocks= (2,2), centers=[(7,7)])
ow = obslib.ChannelWorld(ranges, (2.5,7), 3, 0.2)
# Create the point robot
robot = roblib.Robot(sample_world = world.sample_value, #function handle for collecting observations
start_loc = (5.0, 5.0, 0.0), #where robot is instantiated
extent = ranges, #extent of the explorable environment
kernel_file = None,
kernel_dataset = None,
prior_dataset = None, #(data, observations),
init_lengthscale = 1.0,
init_variance = 100.0,
NOISE = 1.0
world = envlib.Environment(ranges = ranges,
NUM_PTS = 100,
variance = VAR,
lengthscale = LEN,
noise = NOISE,
visualize = True,
seed = SEED,
MAX_COLOR = MAX_COLOR,
MIN_COLOR = MIN_COLOR,
model = gp_world,
obstacle_world = ow)
# Create the evaluation class used to quantify the simulation metrics
evaluation = evalib.Evaluation(world = world, reward_function = REWARD_FUNCTION)
# Generate a prior dataset
'''
x1observe = np.linspace(ranges[0], ranges[1], 5)
x2observe = np.linspace(ranges[2], ranges[3], 5)
x1observe, x2observe = np.meshgrid(x1observe, x2observe, sparse = False, indexing = 'xy')
data = np.vstack([x1observe.ravel(), x2observe.ravel()]).T
observations = world.sample_value(data)
'''
print "Creating robot!"
# Create the point robot
robot = roblib.Robot(sample_world = world.sample_value, #function handle for collecting observations
start_loc = (5.0, 5.0, 0.0), #where robot is instantiated
extent = ranges, #extent of the explorable environment
