def __init__(self, xdim, ydim, world_file = WORLD_FILE):
"""
Create the environment
"""
#self.Qlog = open("Qlog", "wb+")
OpenNero.Environment.__init__(self)
OpenNero.getSimContext().delay = 0.0;
self.XDIM = xdim
self.YDIM = ydim
self.max_steps = 500
self.states = {} # dictionary of agent states
if WORLD_FILE is None:
self.crumbs = world_handler.pattern_cluster(500, "Roomba/world_config.txt")
else: # read external file
data = world_handler.read_world(WORLD_FILE)
self.XDIM = data['dims'][0]
self.YDIM = data['dims'][1]
self.crumbs = data['pellets']
print len(self.crumbs)
# only keep crumbs that are inside the walls
self.crumbs = [c for c in self.crumbs if in_bounds(c.x,c.y)]
self.init_list = AgentInit()
self.init_list.add_type("<class 'Roomba.roomba.RoombaBrain'>")
self.init_list.add_type("<class 'Roomba.RTNEATAgent.RTNEATAgent'>")
self.init_list.add_type("<class 'Roomba.RLAgent.TabularRLAgent'>")
#print self.init_list.types
roomba_abound = self.init_list.get_action("<class 'Roomba.roomba.RoombaBrain'>")
roomba_sbound = self.init_list.get_sensor("<class 'Roomba.roomba.RoombaBrain'>")
roomba_rbound = self.init_list.get_reward("<class 'Roomba.roomba.RoombaBrain'>")
rtneat_abound = self.init_list.get_action("<class 'Roomba.RTNEATAgent.RTNEATAgent'>")
rtneat_sbound = self.init_list.get_sensor("<class 'Roomba.RTNEATAgent.RTNEATAgent'>")
rtneat_rbound = self.init_list.get_reward("<class 'Roomba.RTNEATAgent.RTNEATAgent'>")
### Bounds for Roomba ###
# actions
roomba_abound.add_continuous(-math.pi, math.pi) # amount to turn by
# sensors
roomba_sbound.add_discrete(0,1) # wall bump
roomba_sbound.add_continuous(0,xdim) # self.x
roomba_sbound.add_continuous(0,ydim) # self.y
roomba_sbound.add_continuous(0,xdim) # closest.x
roomba_sbound.add_continuous(0,ydim) # closest.y
# rewards
roomba_rbound.add_continuous(-100,100) # range for reward
### End Bounds for Roomba ####
### Bounds for RTNEAT ###
# actions
rtneat_abound.add_continuous(-math.pi, math.pi) # amount to turn by
#rtneat_abound.add_continuous(0, 1) # signal
# sensors
rtneat_sbound.add_continuous(-math.pi, math.pi) # nearest crumb angle
rtneat_sbound.add_continuous(0, 1) # proportion of crumb nearby
# angle and distance sensors for (up to) 8 nearest neighbors
rtneat_sbound.add_continuous(-math.pi, math.pi)
rtneat_sbound.add_continuous(0, 1)
rtneat_sbound.add_continuous(-math.pi, math.pi)
rtneat_sbound.add_continuous(0, 1)
rtneat_sbound.add_continuous(-math.pi, math.pi)
rtneat_sbound.add_continuous(0, 1)
rtneat_sbound.add_continuous(-math.pi, math.pi)
rtneat_sbound.add_continuous(0, 1)
rtneat_sbound.add_continuous(-math.pi, math.pi)
rtneat_sbound.add_continuous(0, 1)
rtneat_sbound.add_continuous(-math.pi, math.pi)
rtneat_sbound.add_continuous(0, 1)
rtneat_sbound.add_continuous(-math.pi, math.pi)
rtneat_sbound.add_continuous(0, 1)
rtneat_sbound.add_continuous(-math.pi, math.pi)
rtneat_sbound.add_continuous(0, 1)
# rewards
rtneat_rbound.add_continuous(-math.pi, math.pi)
### End Bounds for RTNEAT ###
#------------------------------------------------------------------------
rltabular_abound = self.init_list.get_action("<class 'Roomba.RLAgent.TabularRLAgent'>")
rltabular_sbound = self.init_list.get_sensor("<class 'Roomba.RLAgent.TabularRLAgent'>")
rltabular_rbound = self.init_list.get_reward("<class 'Roomba.RLAgent.TabularRLAgent'>")
### Bounds for RLTabular ###
# actions
rltabular_abound.add_continuous(-math.pi, math.pi) # amount to turn by
# sensors
rltabular_sbound.add_continuous(-1, 1)
rltabular_sbound.add_continuous(-1, 1)
rltabular_sbound.add_continuous(-1, 1)
rltabular_sbound.add_continuous(-1, 1)
rltabular_sbound.add_continuous(-1, 1)
rltabular_sbound.add_continuous(-1, 1)
# rewards
rltabular_rbound.add_continuous(-1, 1)
### End Bounds for RLTabular ###
#------------------------------------------------------------------------
# set up shop
# Add Wayne's Roomba room with experimentally-derived vertical offset to match crumbs.
common.addObject("data/terrain/RoombaRoom.xml", OpenNero.Vector3f(xdim/2,ydim/2, -1), OpenNero.Vector3f(0,0,0), OpenNero.Vector3f(xdim/245.0, ydim/245.0, constants.HEIGHT/24.5), type = constants.OBJECT_TYPE_WALLS)
# OpenNero.getSimContext().addAxes()
self.add_crumbs()
for crumb in self.crumbs:
self.add_crumb_sensors(roomba_sbound)
def __init__(self, xdim, ydim):
"""
Create the environment
"""
OpenNero.Environment.__init__(self)
self.XDIM = xdim
self.YDIM = ydim
self.max_steps = 500
self.states = {} # dictionary of agent states
self.crumbs = world_handler.pattern_cluster(500, "Roomba/world_config.txt")
# only keep crumbs that are inside the walls
self.crumbs = [c for c in self.crumbs if in_bounds(c.x,c.y)]
self.init_list = AgentInit()
self.init_list.add_type("<class 'Roomba.roomba.RoombaBrain'>")
self.init_list.add_type("<class 'Roomba.RTNEATAgent.RTNEATAgent'>")
#print self.init_list.types
roomba_abound = self.init_list.get_action("<class 'Roomba.roomba.RoombaBrain'>")
roomba_sbound = self.init_list.get_sensor("<class 'Roomba.roomba.RoombaBrain'>")
roomba_rbound = self.init_list.get_reward("<class 'Roomba.roomba.RoombaBrain'>")
rtneat_abound = self.init_list.get_action("<class 'Roomba.RTNEATAgent.RTNEATAgent'>")
rtneat_sbound = self.init_list.get_sensor("<class 'Roomba.RTNEATAgent.RTNEATAgent'>")
rtneat_rbound = self.init_list.get_reward("<class 'Roomba.RTNEATAgent.RTNEATAgent'>")
### Bounds for Roomba ###
# actions
roomba_abound.add_continuous(-math.pi, math.pi) # amount to turn by
# sensors
roomba_sbound.add_discrete(0,1) # wall bump
roomba_sbound.add_continuous(0,xdim) # self.x
roomba_sbound.add_continuous(0,ydim) # self.y
roomba_sbound.add_continuous(0,xdim) # closest.x
roomba_sbound.add_continuous(0,ydim) # closest.y
# rewards
roomba_rbound.add_continuous(-100,100) # range for reward
### End Bounds for Roomba ####
### Bounds for RTNEAT ###
# actions
rtneat_abound.add_continuous(-math.pi, math.pi) # amount to turn by
# sensors
rtneat_sbound.add_continuous(-1, 1)
rtneat_sbound.add_continuous(-1, 1)
rtneat_sbound.add_continuous(-1, 1)
rtneat_sbound.add_continuous(-1, 1)
rtneat_sbound.add_continuous(-1, 1)
rtneat_sbound.add_continuous(-1, 1)
# rewards
rtneat_rbound.add_continuous(-1, 1)
### End Bounds for RTNEAT ###
# set up shop
# Add Wayne's Roomba room with experimentally-derived vertical offset to match crumbs.
common.addObject("data/terrain/RoombaRoom.xml", OpenNero.Vector3f(xdim/2,ydim/2, -1), OpenNero.Vector3f(0,0,0), OpenNero.Vector3f(xdim/245.0, ydim/245.0, constants.HEIGHT/24.5), type = constants.OBJECT_TYPE_WALLS)
# OpenNero.getSimContext().addAxes()
self.add_crumbs()
for crumb in self.crumbs:
self.add_crumb_sensors(roomba_sbound)
