def __init__(self, missionXML, serverIp='127.0.0.1'):
self.missionDesc = None
self.mission = None
self.mission_record = None
self.setMissionXML(missionXML)
nAgents = len(missionXML.agentSections)
self.agent_hosts = []
self.agent_hosts += [MalmoPython.AgentHost() for n in range(nAgents)]
self.agent_hosts[0].parse(sys.argv)
if self.receivedArgument('recording_dir'):
recordingsDirectory = malmoutils.get_recordings_directory(
self.agent_hosts[0])
self.mission_record.recordRewards()
self.mission_record.recordObservations()
self.mission_record.recordCommands()
self.mission_record.setDestination(recordingsDirectory + "//" +
"lastRecording.tgz")
if self.agent_hosts[0].receivedArgument("record_video"):
self.mission_record.recordMP4(24, 2000000)
self.client_pool = MalmoPython.ClientPool()
for x in range(10000, 10000 + nAgents):
self.client_pool.add(MalmoPython.ClientInfo(serverIp, x))
self.worldStates = [None] * nAgents
self.observe = [None] * nAgents
self.isAlive = [True] * nAgents
self.frames = [None] * nAgents
self.segmentation_frames = [None] * nAgents
def get_client_pool(self):
my_client_pool = MalmoPython.ClientPool()
my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10000))
# my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 20000))
# my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10002))
# my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10003))
return my_client_pool
def __init__(self, mission_xml, num_agents):
self.mission = MalmoPython.MissionSpec(mission_xml, True)
self.mission_record = MalmoPython.MissionRecordSpec()
self.num_agents = num_agents
self.experiment_ID = str(uuid.uuid4())
self.client_pool = MalmoPython.ClientPool()
for x in range(10000, 10000 + NUM_AGENTS + 1):
self.client_pool.add(MalmoPython.ClientInfo('127.0.0.1', x))
# Create one agent host for parsing
self.agent_hosts = [MalmoPython.AgentHost()]
try:
self.agent_hosts[0].parse(sys.argv)
except RuntimeError as e:
print('ERROR:', e)
print(self.agent_hosts[0].getUsage())
exit(1)
if self.agent_hosts[0].receivedArgument("help"):
print(self.agent_hosts[0].getUsage())
exit(0)
# Create the rest of the agent hosts.
if self.num_agents > 1:
self.agent_hosts += [
MalmoPython.AgentHost() for x in range(NUM_AGENTS - 1)
]
def run_mission(rambo_steve, episode):
agent_host = MalmoPython.AgentHost()
try:
agent_host.parse(sys.argv)
except RuntimeError as e:
print('ERROR:', e)
print(agent_host.getUsage())
exit(1)
if agent_host.receivedArgument('help'):
print(agent_host.getUsage())
my_mission = MalmoPython.MissionSpec(world.getMissionXML(), True)
# adding the recordedFileName into MissionRecordSpec
my_mission_record = MalmoPython.MissionRecordSpec()
# my_mission = malmoutils.get_default_recording_object(agent_host, "Mission")
# adding the spec for adding the recording of the video
# my_mission.requestVideo(1280, 720)
# my_mission_record.recordMP4(30, 2000000)
# set up client to connect:
my_clients = MalmoPython.ClientPool()
for i in range(5):
my_clients.add(
MalmoPython.ClientInfo('127.0.0.1', c.MISSION_CONTROL_PORT + i))
# Attempt to start a mission:
print('Attempting to start mission...')
max_retries = 5
for retry in range(max_retries):
try:
agent_host.startMission(my_mission, my_clients, my_mission_record,
0, "RamboSteve")
break
except RuntimeError as e:
if retry == max_retries - 1:
print('Error starting mission:', e)
exit(1)
else:
time.sleep(2)
# Loop until mission starts:
print('Waiting for the mission to start ', end=' ')
world_state = agent_host.getWorldState()
while not world_state.has_mission_begun:
print('.', end='')
time.sleep(0.1)
world_state = agent_host.getWorldState()
for error in world_state.errors:
print('Error:', error.text)
print()
print('Mission running ', end=' ')
rambo_steve.run(agent_host, episode)
print()
print('Mission ended')
time.sleep(2)
def __init__(self, _):
# graphing the returns
self.log_frequency = 1
self.episode_step = 0
self.episode_return = 0
self.returns = []
self.steps = []
# DISCRETE ACTION SPACE [0, 5]:
# - action 0 = attack
# - action 1 = switch to sword
# - action 2 = switch to axe
# - action 3 = use gapple
# - action 4 = use shield (1 second)
# - action 5 = idle
self.action_space = Discrete(6)
# CONTINUOUS OBSERVATION SPACE:
# - enemy in range: true=1, false=0
# - my health normalized: [0, 1]
# - enemy health normalized: [0, 1]
# - enemy weapon: axe=1, sword=0.75, gapple=0.25, shield=0 (offensive to defensive scale)
# - distance apart from both agents
self.observation_space = Box(0, 1, shape=(5, ), dtype=np.float32)
###################################
# Malmo parameters
self.agent_hosts = [Malmo.AgentHost() for _ in range(2)]
# Create client pool
self.client_pool = Malmo.ClientPool()
self.client_pool.add(Malmo.ClientInfo("127.0.0.1", 10000))
self.client_pool.add(Malmo.ClientInfo("127.0.0.1", 10001))
###################################
# Custom parameters
self.mission_index = 0
###################################
# self-play parameters
#self.opponent_policy = load_trained_agent(CURRENT_CHECKPOINT)
self.use_self_play = False
self.first_reset = True
def get_client_pool(self):
"""
Malmo specific function: To create client pool for connecting to the minecraft server
"""
my_client_pool = MalmoPython.ClientPool()
my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10000))
# my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 20000))
# my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10002))
# my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10003))
return my_client_pool
def initalizeMinecraftMap(xml):
agent_host = MalmoPython.AgentHost()
my_mission = MalmoPython.MissionSpec(xml, True)
recordedFileName = recordPath.format("final_take0_bad.tgz") #comment out to not capture video
#my_mission_record = MalmoPython.MissionRecordSpec()
my_mission_record = MalmoPython.MissionRecordSpec(recordedFileName) #comment out to not capture video
my_mission.requestVideo(1200,720)
my_mission_record.recordMP4(30, 2000000) #comment out to not capture video
my_mission.setViewpoint(1)
my_clients = MalmoPython.ClientPool()
my_clients.add(MalmoPython.ClientInfo('127.0.0.1', 10000)) # add Minecraft machines here as available
return (my_mission,agent_host,my_clients,my_mission_record)
def __init__(self, port=None, existing=False):
self.existing = existing
if not existing:
if not port:
port = InstanceManager._get_valid_port()
cmd = InstanceManager.MC_COMMAND
if InstanceManager.headless:
cmd += " -headless "
cmd += " -port " + str(port)
logger.info("Starting Minecraft process: " + cmd)
args = shlex.split(cmd)
proc = subprocess.Popen(
args,
cwd=InstanceManager.MINECRAFT_DIR,
# pipe entire output
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
# use process group, see http://stackoverflow.com/a/4791612/18576
preexec_fn=os.setsid)
# wait until Minecraft process has outputed "CLIENT enter state: DORMANT"
while True:
line = proc.stdout.readline()
logger.debug(line)
if not line:
raise EOFError(
"Minecraft process finished unexpectedly")
if b"CLIENT enter state: DORMANT" in line:
break
logger.info("Minecraft process ready")
# supress entire output, otherwise the subprocess will block
# NB! there will be still logs under Malmo/Minecraft/run/logs
# FNULL = open(os.devnull, 'w')
FMINE = open('./minecraft.log', 'w')
proc.stdout = FMINE
self.proc = proc
else:
assert port is not None, "No existing port specified."
self.ip = InstanceManager.DEFAULT_IP
self.port = port
self.existing = existing
self.locked = False
# Creating client pool.
logger.info("Creating client pool for {}".format(self))
self.client_pool = MalmoPython.ClientPool()
self.client_pool.add(MalmoPython.ClientInfo(self.ip, self.port))
def __init__(self, _):
# Graphing the returns
self.step_rewards = []
# DISCRETE ACTION SPACE [0, 5]:
# - action 0 = attack
# - action 1 = switch to sword
# - action 2 = switch to axe
# - action 3 = use gapple
# - action 4 = use shield (1 second)
# - action 5 = idle
self.action_space = Discrete(6)
# CONTINUOUS OBSERVATION SPACE:
# - enemy in range: true=1, false=0
# - my health normalized: [0, 1]
# - enemy health normalized: [0, 1]
# - enemy weapon: axe=1, sword=0.75, gapple=0.25, shield=0 (offensive to defensive scale)
self.observation_space = Box(0, 1, shape=(4, ), dtype=np.float32)
###################################
# Malmo parameters
self.agent_hosts = [Malmo.AgentHost() for _ in range(2)]
# Create client pool
self.client_pool = Malmo.ClientPool()
self.client_pool.add(Malmo.ClientInfo("127.0.0.1", 10001))
self.client_pool.add(Malmo.ClientInfo("127.0.0.1", 10002))
self.mission_index = 0
self.old_checkpoint = -1
###################################
# self-play parameters
self.opponent_policy = load_trained_agent(get_current_checkpoint())
self.use_self_play = False
self.last_load = 0
self.first_reset = True
def create_malmo_components():
# setup client pool
client_pool = MalmoPython.ClientPool()
for port in map(int, opts.malmo_ports.split(",")):
print >>sys.stderr, "adding client with port %d" % port
client_pool.add(MalmoPython.ClientInfo("127.0.0.1", port))
# setup agent host
malmo = MalmoPython.AgentHost()
# can't do this without more complex caching of world state vid frames
#malmo.setObservationsPolicy(MalmoPython.ObservationsPolicy.LATEST_OBSERVATION_ONLY)
# load mission spec
mission = MalmoPython.MissionSpec(specs.classroom(opts, overclock_tick_ms), True)
mission_record = MalmoPython.MissionRecordSpec()
# return all
return client_pool, malmo, mission, mission_record
def run(self):
"""Runs the game with the registered agents
Raises:
:class:`jason_malmo.exceptions.NoAgentsException`: There are not registered agents in the game.\n
Register an agent before running the game::
game.register('/path/to/file.asl')
game.run()
"""
self._client_pool = MalmoPython.ClientPool()
if not len(self._agents):
raise NoAgentsException
for port in range(10000, 10000 + len(self._agents) + 1):
self._client_pool.add(MalmoPython.ClientInfo('127.0.0.1', port))
self._my_mission = MalmoPython.MissionSpec(self._get_mission_xml(),
True)
for (index, agent) in enumerate(self._agents):
malmoutils.parse_command_line(agent.malmo_agent)
self._safe_start_mission(
agent.malmo_agent, self._my_mission, self._client_pool,
malmoutils.get_default_recording_object(
agent.malmo_agent, "saved_data"), index, '')
self._safe_wait_for_start(
[agent.malmo_agent for agent in self._agents])
threads = []
for agent in self._agents:
thr = threading.Thread(target=self._jason_env.run_agent,
args=(agent, ),
kwargs={})
thr.start()
threads.append(thr)
# TODO while mission is running
while True:
for agent in self._agents:
for (belief, value) in agent.beliefs.items():
if belief[0] == 'tasks':
tasks = []
for task in list(value)[0].args[0]:
tasks.append(task)
self.tasks.handle(agent, tasks)
time.sleep(0.05)
def StartServer(self, names, ip='127.0.0.1'):
"""
Initiates a server given a mission XML and a list of names of the agents
"""
for i, name in enumerate(names):
n = 10000 + i
self.clientPool.add(MalmoPython.ClientInfo(ip, n))
self.agents.append(MultiAgent(name, self.missionXML, i))
malmoutils.parse_command_line(self.agents[0].host)
for a in self.agents:
a.StartMission(self.clientPool)
self.safeWaitForStart(self.agents)
def start(self):
self.malmo_client_pool = MalmoPython.ClientPool()
self.malmo_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10001)) # 10000 in use - try 10001
self.malmo_mission = MalmoPython.MissionSpec(self.missionXML, True)
self.malmo_mission.forceWorldReset()
self.malmo_mission_record = MalmoPython.MissionRecordSpec()
self.malmo_mission.requestVideo(800, 500)
self.malmo_mission.setViewpoint(1)
# Attempt to start a mission:
max_retries = 10
for retry in range(max_retries):
try:
self.agent_host.startMission(self.malmo_mission, self.malmo_mission_record )
break
except RuntimeError as e:
if retry == max_retries - 1:
print("Error starting mission:",e)
exit(1)
else:
time.sleep(2)
# Loop until mission starts:
print("Waiting for the mission to start ")
self.world_state = self.agent_host.getWorldState()
while not self.world_state.has_mission_begun:
sys.stdout.write(".")
time.sleep(0.1)
self.world_state = self.agent_host.getWorldState()
for error in self.world_state.errors:
print("Error:", error.text)
print(" ")
print("Mission running ")
self.number += 1
self.start_time = time.time()
self.end_time = None
def init_malmo(self):
"""
Initialize new Malmo mission.
"""
# Load the XML file and create mission spec & record.
mission_file = './mission.xml'
with open(mission_file, 'r') as f:
print("Loading mission from %s" % mission_file)
mission_xml = f.read()
my_mission = MalmoPython.MissionSpec(mission_xml, True)
my_mission_record = MalmoPython.MissionRecordSpec()
my_mission.requestVideo(800, 500)
my_mission.setViewpoint(1)
# Attempt to start Malmo.
max_retries = 3
my_clients = MalmoPython.ClientPool()
my_clients.add(MalmoPython.ClientInfo(
'127.0.0.1', 10000)) # add Minecraft machines here as available
for retry in range(max_retries):
try:
self.agent_host.startMission(my_mission, my_clients,
my_mission_record, 0, 'Agent')
break
except RuntimeError as e:
if retry == max_retries - 1:
print("Error starting mission:", e)
exit(1)
else:
time.sleep(2)
# Start the world.
world_state = self.agent_host.getWorldState()
while not world_state.has_mission_begun:
time.sleep(0.1)
world_state = self.agent_host.getWorldState()
for error in world_state.errors:
print("\nError:", error.text)
self.initialize()
return world_state
agent.sendCommand("attack 1")
agent.sendCommand("attack 0")
AGENT_COOLDOWNS[1] = ATTACK_COOLDOWNS[AGENT_WEAPONS[1]]
if __name__ == "__main__":
# Flush immediately
print = functools.partial(print, flush=True)
# Create agent host
agent_hosts = [Malmo.AgentHost() for _ in range(AGENT_COUNT)]
# Create client pool
client_pool = Malmo.ClientPool()
client_pool.add(Malmo.ClientInfo("127.0.0.1", 10000))
client_pool.add(Malmo.ClientInfo("127.0.0.1", 10002))
for a in range(MISSION_COUNT):
print(f"Running mission #{a}...")
# Create missions
mission = Malmo.MissionSpec(get_mission_xml(), True)
mission_id = str(uuid.uuid4())
# Start mission
for a in range(AGENT_COUNT):
start_mission(agent_hosts[a], mission, client_pool,
Malmo.MissionRecordSpec(), a, mission_id)
wait_for_start(agent_hosts)
<AgentHandlers>
<DiscreteMovementCommands/>
<RewardForCollectingItem>
<Item reward="10" type="dirt"/>
</RewardForCollectingItem>
<RewardForDiscardingItem>
<Item reward="100" type="dirt"/>
</RewardForDiscardingItem>
</AgentHandlers>
</AgentSection>
</Mission>'''
my_mission = MalmoPython.MissionSpec(xml,True)
client_pool = MalmoPython.ClientPool()
client_pool.add( MalmoPython.ClientInfo('127.0.0.1',10000) )
client_pool.add( MalmoPython.ClientInfo('127.0.0.1',10001) )
MalmoPython.setLogging("", MalmoPython.LoggingSeverityLevel.LOG_OFF)
def safeStartMission(agent_host, mission, client_pool, recording, role, experimentId):
used_attempts = 0
max_attempts = 5
print("Calling startMission for role", role)
while True:
try:
agent_host.startMission(mission, client_pool, recording, role, experimentId)
break
except MalmoPython.MissionException as e:
errorCode = e.details.errorCode
if errorCode == MalmoPython.MissionErrorCode.MISSION_SERVER_WARMING_UP:
</AgentHandlers>
</AgentSection>'''
xml += '</Mission>'
return xml
# Set up a client pool.
# IMPORTANT: If ANY of the clients will be on a different machine, then you MUST
# make sure that any client which can be the server has an IP address that is
# reachable from other machines - ie DO NOT SIMPLY USE 127.0.0.1!!!!
# The IP address used in the client pool will be broadcast to other agents who
# are attempting to find the server - so this will fail for any agents on a
# different machine.
client_pool = MalmoPython.ClientPool()
for x in range(10000, 10000 + NUM_AGENTS + 1):
client_pool.add( MalmoPython.ClientInfo('127.0.0.1', x) )
# Keep score of how our robots are doing:
survival_scores = [0 for x in range(NUM_AGENTS)] # Lasted to the end of the mission without dying.
apple_scores = [0 for x in range(NUM_AGENTS)] # Collecting apples is good.
zombie_kill_scores = [0 for x in range(NUM_AGENTS)] # Good! Help rescue humanity from zombie-kind.
player_kill_scores = [0 for x in range(NUM_AGENTS)] # Bad! Don't kill the other players!
num_missions = 5 if INTEGRATION_TEST_MODE else 30000
for mission_no in range(1, num_missions+1):
print("Running mission #" + str(mission_no))
# Create mission xml - use forcereset if this is the first mission.
my_mission = MalmoPython.MissionSpec(getXML("true" if mission_no == 1 else "false"), True)
# Generate an experiment ID for this mission.
# This is used to make sure the right clients join the right servers -
def run(argv=['']):
if "MALMO_XSD_PATH" not in os.environ:
print("Please set the MALMO_XSD_PATH environment variable.")
return
malmoutils.fix_print()
agent_host = MalmoPython.AgentHost()
malmoutils.parse_command_line(agent_host, argv)
my_mission = MalmoPython.MissionSpec()
my_mission.timeLimitInSeconds( 10 )
my_mission.requestVideo( 320, 240 )
my_mission.rewardForReachingPosition( 19.5, 0.0, 19.5, 100.0, 1.1 )
my_mission_record = malmoutils.get_default_recording_object(agent_host, "saved_data")
# client_info = MalmoPython.ClientInfo('localhost', 10000)
client_info = MalmoPython.ClientInfo('127.0.0.1', 10000)
pool = MalmoPython.ClientPool()
pool.add(client_info)
experiment_id = str(uuid.uuid1())
print("experiment id " + experiment_id)
max_retries = 3
max_response_time = 60 # seconds
for retry in range(max_retries):
try:
agent_host.startMission(my_mission, pool, my_mission_record, 0, experiment_id)
break
except RuntimeError as e:
if retry == max_retries - 1:
print("Error starting mission:",e)
exit(1)
else:
time.sleep(2)
print("Waiting for the mission to start", end=' ')
start_time = time.time()
world_state = agent_host.getWorldState()
while not world_state.has_mission_begun:
print(".", end="")
time.sleep(0.1)
if time.time() - start_time > max_response_time:
print("Max delay exceeded for mission to begin")
restart_minecraft(world_state, agent_host, client_info, "begin mission")
world_state = agent_host.getWorldState()
for error in world_state.errors:
print("Error:",error.text)
print()
last_delta = time.time()
# main loop:
while world_state.is_mission_running:
agent_host.sendCommand( "move 1" )
agent_host.sendCommand( "turn " + str(random.random()*2-1) )
time.sleep(0.5)
world_state = agent_host.getWorldState()
print("video,observations,rewards received:",world_state.number_of_video_frames_since_last_state,world_state.number_of_observations_since_last_state,world_state.number_of_rewards_since_last_state)
if (world_state.number_of_video_frames_since_last_state > 0 or
world_state.number_of_observations_since_last_state > 0 or
world_state.number_of_rewards_since_last_state > 0):
last_delta = time.time()
else:
if time.time() - last_delta > max_response_time:
print("Max delay exceeded for world state change")
restart_minecraft(world_state, agent_host, client_info, "world state change")
for reward in world_state.rewards:
print("Summed reward:",reward.getValue())
for error in world_state.errors:
print("Error:",error.text)
for frame in world_state.video_frames:
print("Frame:",frame.width,'x',frame.height,':',frame.channels,'channels')
#image = Image.frombytes('RGB', (frame.width, frame.height), bytes(frame.pixels) ) # to convert to a PIL image
print("Mission has stopped.")
def deep_q_learning_run(sess,
agent_host,
q_estimator,
state_processor,
experiment_dir,
epsilon_start=1.0,
epsilon_end=0.1,
epsilon_decay_steps=8000):
"""
Q-Learning algorithm for off-policy TD control using Function Approximation.
Finds the optimal greedy policy while following an epsilon-greedy policy.
Args:
sess: Tensorflow Session object
env: OpenAI environment
q_estimator: Estimator object used for the q values
target_estimator: Estimator object used for the targets
state_processor: A StateProcessor object
num_episodes: Number of episodes to run for
experiment_dir: Directory to save Tensorflow summaries in
replay_memory_size: Size of the replay memory
replay_memory_init_size: Number of random experiences to sampel when initializing
the reply memory.
update_target_estimator_every: Copy parameters from the Q estimator to the
target estimator every N steps
discount_factor: Gamma discount factor
epsilon_start: Chance to sample a random action when taking an action.
Epsilon is decayed over time and this is the start value
epsilon_end: The final minimum value of epsilon after decaying is done
epsilon_decay_steps: Number of steps to decay epsilon over
batch_size: Size of batches to sample from the replay memory
record_video_every: Record a video every N episodes
Returns:
An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.
"""
mission_file = agent_host.getStringArgument('mission_file')
with open(mission_file, 'r') as f:
print("Loading mission from %s" % mission_file)
mission_xml = f.read()
my_mission = MalmoPython.MissionSpec(mission_xml, True)
my_mission.removeAllCommandHandlers()
my_mission.allowAllDiscreteMovementCommands()
my_mission.setViewpoint(2)
my_clients = MalmoPython.ClientPool()
my_clients.add(MalmoPython.ClientInfo(
'127.0.0.1', 10000)) # add Minecraft machines here as available
max_retries = 3
agentID = 0
expID = 'Deep_q_learning memory'
# Create directories for checkpoints and summaries
checkpoint_dir = os.path.join(experiment_dir, "checkpoints")
print("Checkpoint dir is:", checkpoint_dir)
saver = tf.train.Saver()
# Load a previous checkpoint if we find one
# latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
# print("~~~~~~~~~~~~~~", latest_checkpoint)
# exit(0)
latest_checkpoint = os.path.join(checkpoint_dir, "model")
if latest_checkpoint:
print("Loading model checkpoint {}...\n".format(latest_checkpoint))
saver.restore(sess, latest_checkpoint)
total_t = sess.run(tf.contrib.framework.get_global_step())
# The epsilon decay schedule
epsilons = np.linspace(epsilon_start, epsilon_end, epsilon_decay_steps)
# The policy we're following
policy = make_epsilon_greedy_policy(q_estimator, len(actionSet))
my_mission_record = malmoutils.get_default_recording_object(
agent_host, "save_%s-rep" % (expID))
for retry in range(max_retries):
try:
agent_host.startMission(my_mission, my_clients, my_mission_record,
agentID, "%s" % (expID))
break
except RuntimeError as e:
if retry == max_retries - 1:
print("Error starting mission:", e)
exit(1)
else:
time.sleep(2.5)
world_state = agent_host.getWorldState()
while not world_state.has_mission_begun:
print(".", end="")
time.sleep(0.1)
print("Sleeping")
world_state = agent_host.getWorldState()
for error in world_state.errors:
print("Error:", error.text)
print()
agent_host.sendCommand("look -1")
agent_host.sendCommand("look -1")
while world_state.is_mission_running and all(
e.text == '{}' for e in world_state.observations):
print("Sleeping....")
world_state = agent_host.peekWorldState()
# Populate the replay memory with initial experience
while world_state.number_of_observations_since_last_state <= 0 and world_state.is_mission_running:
# print("Sleeping")
time.sleep(0.1)
world_state = agent_host.peekWorldState()
state = gridProcess(
world_state
) # MALMO ENVIRONMENT Grid world NEEDED HERE/ was env.reset()
state = state_processor.process(sess, state)
state = np.stack([state] * 4, axis=2)
stepNum = 0
while world_state.is_mission_running:
action = randint(0, 3)
print("actions:", action)
# next_state, reward, done, _ = env.step(actionSet[action]) # Malmo send command for the action
# print("Sending command: ", actionSet[action])
print("Step %s" % stepNum)
stepNum += 1
agent_host.sendCommand(actionSet[action])
world_state = agent_host.peekWorldState()
num_frames_seen = world_state.number_of_video_frames_since_last_state
while world_state.is_mission_running and world_state.number_of_video_frames_since_last_state == num_frames_seen:
world_state = agent_host.peekWorldState()
if world_state.is_mission_running:
# Getting the reward from taking a step
while world_state.number_of_observations_since_last_state <= 0:
time.sleep(0.1)
world_state = agent_host.peekWorldState()
# world_state = agent_host.getWorldState()
next_state = gridProcess(world_state)
next_state = state_processor.process(sess, next_state)
next_state = np.append(state[:, :, 1:],
np.expand_dims(next_state, 2),
axis=2)
state = next_state
# time.sleep(1)
return None
def __init__(self,
world_def,
video_dim=(32, 32),
num_parallel=1,
time_limit=20,
reset=True,
discrete_actions=False,
vision_observation=False,
depth=False,
num_frames=1,
grayscale=True):
self.video_width, self.video_height = video_dim
self.image_width, self.image_height = video_dim
self.discrete_actions = discrete_actions
self.vision_observation = vision_observation
self.depth = depth
self.num_parallel = num_parallel
self.world_def = world_def
self.mission = self.world_def.generate_mission(reset=reset)
#self.XGoalPos, self.YGoalPos = self.world_def.goal_pos[0], self.world_def.goal_pos[2]
self.mission.requestVideo(self.video_height, self.video_width)
self.mission.observeRecentCommands()
self.mission.allowAllContinuousMovementCommands()
self.mission.timeLimitInSeconds(time_limit)
if self.num_parallel > 1:
self.client_pool = MalmoPython.ClientPool()
for i in range(num_parallel):
port = 10000 + i
self.client_pool.add(MalmoPython.ClientInfo("127.0.0.1", port))
self.agent_host = MalmoPython.AgentHost()
self.agent_host.setObservationsPolicy(
MalmoPython.ObservationsPolicy.KEEP_ALL_OBSERVATIONS)
# self.agent_host.setObservationsPolicy(MalmoPython.ObservationsPolicy.LATEST_OBSERVATION_ONLY)
#self.agent_host.setVideoPolicy(MalmoPython.VideoPolicy.KEEP_ALL_FRAMES)
self.agent_host.setVideoPolicy(
MalmoPython.VideoPolicy.LATEST_FRAME_ONLY)
self.mission_record_spec = MalmoPython.MissionRecordSpec()
if discrete_actions:
self._action_set = {
0: "move 1",
1: "turn 0.5",
2: "turn -0.5",
3: None
}
self.action_space = Discrete(n=len(self._action_set))
else:
self._action_set = [
("move", (-1, 1)),
("turn", (-1, 1)),
("pitch", (-1, 1)),
("use", (0, 1)),
("jump", (0, 1)),
]
# self._action_set = [("move", (0, 1)),
# ("move", (-1, 0)),
# ("turn", (0, 1)),
# ("turn", (-1, 0))]
lower_bound = np.asarray([x[1][0] for x in self._action_set])
upper_bound = np.asarray([x[1][1] for x in self._action_set])
self.action_space = Box(lower_bound, upper_bound)
self.num_frames = num_frames
self.grayscale = grayscale
if self.grayscale:
self.num_frame_channels = 1
high = 1
else:
self.num_frame_channels = 3
high = 255
# Obs keys and bounds
x_bounds = self.world_def.x_bounds
z_bounds = self.world_def.z_bounds
self.max_dist = np.linalg.norm((x_bounds[-1], z_bounds[-1]))
self.minDistanceFromGoal = None
if self.vision_observation:
self.observation_space = Box(
low=0,
high=high,
shape=(self.image_height, self.image_width,
self.num_frames * self.num_frame_channels))
else:
self.obs_keys = [
(u'XPos', x_bounds),
(u'YPos', (200, 300)),
(u'ZPos', z_bounds),
(u'yaw', (0, 360)),
(u'pitch', (0, 180)),
#(u'XGoalPos', x_bounds),
#(u'YGoalPos', z_bounds),
(u'DistanceTravelled', (0, 30)),
(u'distanceFromGoal', (0, self.max_dist))
]
l_bounds = [key[1][0] for key in self.obs_keys]
u_bounds = [key[1][1] for key in self.obs_keys]
self.observation_space = Box(np.array(l_bounds),
np.array(u_bounds))
self.last_obs = None
self.cum_reward = 0
self.distance_travelled = 0
self.terminal = False
self.jump = 0
def init(self,
client_pool=None,
start_minecraft=None,
continuous_discrete=True,
add_noop_command=None,
max_retries=90,
retry_sleep=10,
step_sleep=0.001,
skip_steps=0,
videoResolution=None,
videoWithDepth=None,
observeRecentCommands=None,
observeHotBar=None,
observeFullInventory=None,
observeGrid=None,
observeDistance=None,
observeChat=None,
allowContinuousMovement=None,
allowDiscreteMovement=None,
allowAbsoluteMovement=None,
recordDestination=None,
recordObservations=None,
recordRewards=None,
recordCommands=None,
recordMP4=None,
gameMode=None,
forceWorldReset=None):
self.max_retries = max_retries
self.retry_sleep = retry_sleep
self.step_sleep = step_sleep
self.skip_steps = skip_steps
self.forceWorldReset = forceWorldReset
self.continuous_discrete = continuous_discrete
self.add_noop_command = add_noop_command
if videoResolution:
if videoWithDepth:
self.mission_spec.requestVideoWithDepth(*videoResolution)
else:
self.mission_spec.requestVideo(*videoResolution)
if observeRecentCommands:
self.mission_spec.observeRecentCommands()
if observeHotBar:
self.mission_spec.observeHotBar()
if observeFullInventory:
self.mission_spec.observeFullInventory()
if observeGrid:
self.mission_spec.observeGrid(*(observeGrid + ["grid"]))
if observeDistance:
self.mission_spec.observeDistance(*(observeDistance + ["dist"]))
if observeChat:
self.mission_spec.observeChat()
if allowContinuousMovement or allowDiscreteMovement or allowAbsoluteMovement:
# if there are any parameters, remove current command handlers first
self.mission_spec.removeAllCommandHandlers()
if allowContinuousMovement is True:
self.mission_spec.allowAllContinuousMovementCommands()
elif isinstance(allowContinuousMovement, list):
for cmd in allowContinuousMovement:
self.mission_spec.allowContinuousMovementCommand(cmd)
if allowDiscreteMovement is True:
self.mission_spec.allowAllDiscreteMovementCommands()
elif isinstance(allowDiscreteMovement, list):
for cmd in allowDiscreteMovement:
self.mission_spec.allowDiscreteMovementCommand(cmd)
if allowAbsoluteMovement is True:
self.mission_spec.allowAllAbsoluteMovementCommands()
elif isinstance(allowAbsoluteMovement, list):
for cmd in allowAbsoluteMovement:
self.mission_spec.allowAbsoluteMovementCommand(cmd)
if start_minecraft:
# start Minecraft process assigning port dynamically
self.mc_process, port = minecraft_py.start()
logger.info(
"Started Minecraft on port %d, overriding client_pool.", port)
client_pool = [('127.0.0.1', port)]
if client_pool:
if not isinstance(client_pool, list):
raise ValueError(
"client_pool must be list of tuples of (IP-address, port)")
self.client_pool = MalmoPython.ClientPool()
for client in client_pool:
self.client_pool.add(MalmoPython.ClientInfo(*client))
# TODO: produce observation space dynamically based on requested features
self.video_height = self.mission_spec.getVideoHeight(0)
self.video_width = self.mission_spec.getVideoWidth(0)
self.video_depth = self.mission_spec.getVideoChannels(0)
self.observation_space = spaces.Box(low=0,
high=255,
shape=(self.video_height,
self.video_width,
self.video_depth))
# dummy image just for the first observation
self.last_image = np.zeros(
(self.video_height, self.video_width, self.video_depth),
dtype=np.uint8)
self._create_action_space()
# mission recording
self.mission_record_spec = MalmoPython.MissionRecordSpec(
) # record nothing
if recordDestination:
self.mission_record_spec.setDestination(recordDestination)
if recordRewards:
self.mission_record_spec.recordRewards()
if recordCommands:
self.mission_record_spec.recordCommands()
if recordMP4:
self.mission_record_spec.recordMP4(*recordMP4)
if gameMode:
if gameMode == "spectator":
self.mission_spec.setModeToSpectator()
elif gameMode == "creative":
self.mission_spec.setModeToCreative()
elif gameMode == "survival":
logger.warn(
"Cannot force survival mode, assuming it is the default.")
else:
assert False, "Unknown game mode: " + gameMode
def main():
#Hardcode number of agents to play song
num_agents = 4
#Obtain song csv and get solutions
#freq_list = mt.create_note_list("Twinkle_Twinkle_Little_Star.csv",120,7000,-.08) #1 Agent
#freq_list = mt.create_note_list("Chopsticks.csv",120,4000,-.15,.03) #2 Agents
freq_list = mt.create_note_list("Bad_Apple.csv", 120, 3000, -.08,
.03) #2 Agents
#freq_list = mt.create_note_list("Grenade_120BPM.csv",120,1500,-.08,.03) #4 Agents
freq_list = mt.number_converter(freq_list)
solutions = cs.get_solutions(freq_list, num_agents)
print(solutions)
#print(solutions)
#Get Mission. Needed for teleport positions.
missionXML = getMissionXML(num_agents)
#Create musician for each agent and pass teleport positions.
musicians = []
for i in range(num_agents):
agent_positions = generateAgentTeleportPositions(note_positions, i)
musicians.append(Musician(agent_positions))
'''
MALMO
'''
print('Starting...', flush=True)
#Create agents.
agent_hosts = []
for i in range(num_agents):
agent_hosts.append(MalmoPython.AgentHost())
malmoutils.parse_command_line(agent_hosts[0])
#Get mission and allow commands for teleport.
my_mission = MalmoPython.MissionSpec(missionXML, True)
my_mission.allowAllChatCommands()
#Add client for each agent needed.
my_client_pool = MalmoPython.ClientPool()
for i in range(num_agents):
my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10000 + i))
MalmoPython.setLogging("", MalmoPython.LoggingSeverityLevel.LOG_OFF)
#Start mission for each agent
for i in range(num_agents):
startMission(
agent_hosts[i], my_mission, my_client_pool,
malmoutils.get_default_recording_object(
agent_hosts[0], "agent_" + str(i + 1) + "_viewpoint_discrete"),
i, '')
#Wait for all missions to begin.
waitForStart(agent_hosts)
#Pause for simulation to begin.
time.sleep(1)
'''
SIMULATION BEGINS HERE
'''
for i in range(len(solutions[0])):
#teleport each agent to the corresponding note.
for j in range(len(musicians)):
musicians[j].teleport_to_noteblock(agent_hosts[j], solutions[j][i])
# play each note.
for k in range(len(musicians)):
if musicians[k].can_play:
agent_hosts[k].sendCommand("attack 1")
time.sleep(0.001)
for k in range(len(musicians)):
if musicians[k].can_play:
agent_hosts[k].sendCommand("attack 0")
musicians[k].can_play = False
#modifies the timing between each note hit.
time.sleep(0.2)
if actions[step] == 4: nextx = x - 1
if table[nextx][nextz] == 1: return False
# if abs(actions[step] - actions[step - 1]) == 2:
# return False
# if actions[step] == 1: z = z + 1
# if actions[step] == 2: x = x + 1
# if actions[step] == 3: z = z - 1
# if actions[step] == 4: x = x - 1
# table[x][z] = 1
return True
my_mission = MalmoPython.MissionSpec(missionXML, True)
my_mission_record = MalmoPython.MissionRecordSpec()
my_client_pool = MalmoPython.ClientPool()
my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1",
10001)) #10000 in use - try 10001
# Attempt to start a mission:
max_retries = 3
for retry in range(max_retries):
try:
#agent_host.startMission(my_mission, my_mission_record)
agent_host.startMission(my_mission, my_client_pool, my_mission_record,
0, "experimentID2")
break
except RuntimeError as e:
if retry == max_retries - 1:
print("Error starting mission:", e)
exit(1)
else:
time.sleep(2)
def deep_q_learning(sess,
agent_host,
q_estimator,
target_estimator,
state_processor,
num_episodes,
experiment_dir,
replay_memory_size=500000,
replay_memory_init_size=50000,
update_target_estimator_every=10000,
discount_factor=0.99,
epsilon_start=1.0,
epsilon_end=0.1,
epsilon_decay_steps=50000,
batch_size=32,
record_video_every=100):
"""
Q-Learning algorithm for off-policy TD control using Function Approximation.
Finds the optimal greedy policy while following an epsilon-greedy policy.
Args:
sess: Tensorflow Session object
env: OpenAI environment
q_estimator: Estimator object used for the q values
target_estimator: Estimator object used for the targets
state_processor: A StateProcessor object
num_episodes: Number of episodes to run for
experiment_dir: Directory to save Tensorflow summaries in
replay_memory_size: Size of the replay memory
replay_memory_init_size: Number of random experiences to sampel when initializing
the reply memory.
update_target_estimator_every: Copy parameters from the Q estimator to the
target estimator every N steps
discount_factor: Gamma discount factor
epsilon_start: Chance to sample a random action when taking an action.
Epsilon is decayed over time and this is the start value
epsilon_end: The final minimum value of epsilon after decaying is done
epsilon_decay_steps: Number of steps to decay epsilon over
batch_size: Size of batches to sample from the replay memory
record_video_every: Record a video every N episodes
Returns:
An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.
"""
mission_file = agent_host.getStringArgument('mission_file')
with open(mission_file, 'r') as f:
print("Loading mission from %s" % mission_file)
mission_xml = f.read()
my_mission = MalmoPython.MissionSpec(mission_xml, True)
my_mission.removeAllCommandHandlers()
my_mission.allowAllDiscreteMovementCommands()
my_mission.setViewpoint(2)
my_clients = MalmoPython.ClientPool()
my_clients.add(MalmoPython.ClientInfo('127.0.0.1', 10000)) # add Minecraft machines here as available
max_retries = 3
agentID = 0
expID = 'Deep_q_learning memory'
Transition = namedtuple("Transition", ["state", "action", "reward", "next_state", "done"])
# The replay memory
replay_memory = []
# Keeps track of useful statistics
stats = plotting.EpisodeStats(
episode_lengths=np.zeros(num_episodes),
episode_rewards=np.zeros(num_episodes))
# Create directories for checkpoints and summaries
checkpoint_dir = os.path.join(experiment_dir, "checkpoints")
checkpoint_path = os.path.join(checkpoint_dir, "model")
monitor_path = os.path.join(experiment_dir, "monitor")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
if not os.path.exists(monitor_path):
os.makedirs(monitor_path)
saver = tf.train.Saver()
# Load a previous checkpoint if we find one
latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
if latest_checkpoint:
print("Loading model checkpoint {}...\n".format(latest_checkpoint))
saver.restore(sess, latest_checkpoint)
total_t = sess.run(tf.contrib.framework.get_global_step())
# The epsilon decay schedule
epsilons = np.linspace(epsilon_start, epsilon_end, epsilon_decay_steps)
# The policy we're following
policy = make_epsilon_greedy_policy(
q_estimator,
len(actionSet))
my_mission_record = malmoutils.get_default_recording_object(agent_host,
"./save_%s-rep" % (expID))
for retry in range(max_retries):
try:
agent_host.startMission(my_mission, my_clients, my_mission_record, agentID, "%s" %(expID))
break
except RuntimeError as e:
if retry == max_retries - 1:
print("Error starting mission:", e)
exit(1)
else:
time.sleep(2.5)
world_state = agent_host.getWorldState()
while not world_state.has_mission_begun:
print(".", end="")
time.sleep(0.1)
print("Sleeping")
world_state = agent_host.getWorldState()
for error in world_state.errors:
print("Error:", error.text)
print()
# world_state = agent_host.peekWorldState()
while world_state.is_mission_running and all(e.text == '{}' for e in world_state.observations):
print("Sleeping....")
world_state = agent_host.peekWorldState()
world_state = agent_host.getWorldState()
# Populate the replay memory with initial experience
print("Populating replay memory...")
while world_state.number_of_observations_since_last_state <= 0:
# print("Sleeping")
time.sleep(0.1)
world_state = agent_host.peekWorldState()
state = gridProcess(world_state) #MALMO ENVIRONMENT Grid world NEEDED HERE/ was env.reset()
state = state_processor.process(sess, state)
state = np.stack([state] * 4, axis=2)
for i in range(replay_memory_init_size):
print("%s th replay memory" %i)
action_probs = policy(sess, state, epsilons[min(total_t, epsilon_decay_steps-1)])
action = np.random.choice(np.arange(len(action_probs)), p=action_probs)
# next_state, reward, done, _ = env.step(actionSet[action]) # Malmo send command for the action
# print("Sending command: ", actionSet[action])
agent_host.sendCommand(actionSet[action])
#checking if the mission is done
world_state = agent_host.peekWorldState()
#Getting the reward from taking a step
if world_state.number_of_rewards_since_last_state > 0:
reward = world_state.rewards[-1].getValue()
print("Just received the reward: %s on action: %s "%(reward, actionSet[action]))
else:
print("No reward")
reward = 0
#getting the next state
while world_state.number_of_observations_since_last_state <=0 and world_state.is_mission_running:
print("Sleeping")
time.sleep(0.1)
world_state = agent_host.peekWorldState()
if world_state.is_mission_running:
next_state = gridProcess(world_state)
next_state = state_processor.process(sess, next_state)
next_state = np.append(state[:,:,1:], np.expand_dims(next_state, 2), axis=2)
done = not world_state.is_mission_running
replay_memory.append(Transition(state, action, reward, next_state, done))
state = next_state
else:
for retry in range(max_retries):
try:
agent_host.startMission(my_mission, my_clients, my_mission_record, agentID, "%s" % (expID))
break
except RuntimeError as e:
if retry == max_retries - 1:
print("Error starting mission:", e)
exit(1)
else:
time.sleep(2.5)
world_state = agent_host.getWorldState()
while not world_state.has_mission_begun:
print(".", end="")
time.sleep(0.1)
world_state = agent_host.getWorldState()
world_state = agent_host.peekWorldState()
while world_state.is_mission_running and all(e.text == '{}' for e in world_state.observations):
world_state = agent_host.peekWorldState()
world_state = agent_host.getWorldState()
if not world_state.is_mission_running:
print("Breaking")
break
state = gridProcess(world_state) # Malmo GetworldState? / env.reset()
state = state_processor.process(sess, state)
state = np.stack([state] * 4, axis=2)
print("Finished populating memory")
# Record videos
# Use the gym env Monitor wrapper
# env = Monitor(env,
# directory=monitor_path,
# resume=True,
# video_callable=lambda count: count % record_video_every ==0)
# NEED TO RECORD THE VIDEO AND SAVE TO THE SPECIFIED DIRECTORY
for i_episode in range(num_episodes):
print("%s-th episode"%i_episode)
if i_episode != 0:
mission_file = agent_host.getStringArgument('mission_file')
with open(mission_file, 'r') as f:
print("Loading mission from %s" % mission_file)
mission_xml = f.read()
my_mission = MalmoPython.MissionSpec(mission_xml, True)
my_mission.removeAllCommandHandlers()
my_mission.allowAllDiscreteMovementCommands()
# my_mission.requestVideo(320, 240)
my_mission.forceWorldReset()
my_mission.setViewpoint(2)
my_clients = MalmoPython.ClientPool()
my_clients.add(MalmoPython.ClientInfo('127.0.0.1', 10000)) # add Minecraft machines here as available
max_retries = 3
agentID = 0
expID = 'Deep_q_learning '
my_mission_record = malmoutils.get_default_recording_object(agent_host,
"./save_%s-rep%d" % (expID, i))
for retry in range(max_retries):
try:
agent_host.startMission(my_mission, my_clients, my_mission_record, agentID, "%s-%d" % (expID, i))
break
except RuntimeError as e:
if retry == max_retries - 1:
print("Error starting mission:", e)
exit(1)
else:
time.sleep(2.5)
world_state = agent_host.getWorldState()
print("Waiting for the mission to start", end=' ')
while not world_state.has_mission_begun:
print(".", end="")
time.sleep(0.1)
world_state = agent_host.getWorldState()
for error in world_state.errors:
print("Error:", error.text)
# Save the current checkpoint
saver.save(tf.get_default_session(), checkpoint_path)
# world_state = agent_host.getWorldState()
# Reset the environment
# world_state = agent_host.peekWorldState()
while world_state.is_mission_running and all(e.text == '{}' for e in world_state.observations):
# print("Sleeping!!!")
world_state = agent_host.peekWorldState()
world_state = agent_host.getWorldState()
state = gridProcess(world_state) #MalmoGetWorldState?
state = state_processor.process(sess, state)
state = np.stack([state] * 4, axis=2)
loss = None
# One step in the environment
for t in itertools.count():
# Epsilon for this time step
epsilon = epsilons[min(total_t, epsilon_decay_steps-1)]
# Add epsilon to Tensorboard
episode_summary = tf.Summary()
episode_summary.value.add(simple_value=epsilon, tag="epsilon")
q_estimator.summary_writer.add_summary(episode_summary, total_t)
# Maybe update the target estimator
if total_t % update_target_estimator_every == 0:
copy_model_parameters(sess, q_estimator, target_estimator)
print("\nCopied model parameters to target network.")
# Print out which step we're on, useful for debugging.
print("\rStep {} ({}) @ Episode {}/{}, loss: {}".format(
t, total_t, i_episode + 1, num_episodes, loss), end="")
sys.stdout.flush()
# Take a step
action_probs = policy(sess, state, epsilon)
action = np.random.choice(np.arange(len(action_probs)), p=action_probs)
# next_state, reward, done, _ = env.step(actionSet[action]) # Malmo AgentHost send command?
# print("Sending command: ", actionSet[action])
agent_host.sendCommand(actionSet[action])
world_state = agent_host.peekWorldState()
if world_state.number_of_rewards_since_last_state > 0:
reward = world_state.rewards[-1].getValue()
print("Just received the reward: %s on action: %s " % (reward, actionSet[action]))
else:
print("No reward")
reward = 0
while world_state.is_mission_running and all(e.text == '{}' for e in world_state.observations):
# print("Sleeping!!!")
world_state = agent_host.peekWorldState()
# world_state = agent_host.getWorldState()
# if not world_state.is_mission_running:
# print("Breaking")
# break
done = not world_state.is_mission_running
print(" IS MISSION FINISHED? ", done)
# if done:
# print("Breaking before updating last reward")
# break
next_state = gridProcess(world_state)
next_state = state_processor.process(sess, next_state)
next_state = np.append(state[:,:,1:], np.expand_dims(next_state, 2), axis=2)
# If our replay memory is full, pop the first element
if len(replay_memory) == replay_memory_size:
replay_memory.pop(0)
# Save transition to replay memory
replay_memory.append(Transition(state, action, reward, next_state, done))
# Update statistics
stats.episode_rewards[i_episode] += reward
stats.episode_lengths[i_episode] = t
# Sample a minibatch from the replay memory
samples = random.sample(replay_memory, batch_size)
states_batch, action_batch, reward_batch, next_states_batch, done_batch = map(np.array, zip(*samples))
# Calculate q values and targets (Double DQN)
q_values_next = q_estimator.predict(sess, next_states_batch)
best_actions = np.argmax(q_values_next, axis=1)
q_values_next_target = target_estimator.predict(sess, next_states_batch)
targets_batch = reward_batch + np.invert(done_batch).astype(np.float32) * \
discount_factor * q_values_next_target[np.arange(batch_size), best_actions]
# Perform gradient descent update
states_batch = np.array(states_batch)
loss = q_estimator.update(sess, states_batch, action_batch, targets_batch)
if done:
print("End of Episode")
break
state = next_state
total_t += 1
# Add summaries to tensorboard
episode_summary = tf.Summary()
episode_summary.value.add(simple_value=stats.episode_rewards[i_episode], node_name="episode_reward", tag="episode_reward")
episode_summary.value.add(simple_value=stats.episode_lengths[i_episode], node_name="episode_length", tag="episode_length")
q_estimator.summary_writer.add_summary(episode_summary, total_t)
q_estimator.summary_writer.flush()
yield total_t, plotting.EpisodeStats(
episode_lengths=stats.episode_lengths[:i_episode+1],
episode_rewards=stats.episode_rewards[:i_episode+1])
# env.monitor.close()
return stats
validate = True
agent_host = MalmoPython.AgentHost()
try:
agent_host.parse(sys.argv)
except RuntimeError as e:
print 'ERROR:', e
print agent_host.getUsage()
exit(1)
if agent_host.receivedArgument("help"):
print agent_host.getUsage()
exit(0)
# Create a pool of Minecraft Mod clients:
my_client_pool = MalmoPython.ClientPool()
# Add the default client - port 10000 on the local machine:
my_client = MalmoPython.ClientInfo("127.0.0.1", 10000)
my_client_pool.add(my_client)
# Add extra clients here:
# eg my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10001)) etc
# Create a unique identifier - different each time this script is run.
# In multi-agent missions all agents must pass the same experimentID, in order to prevent agents from joining the wrong experiments.
experimentID = uuid.uuid4()
# Create a folder to put our recordings - the platform will not create missing folders itself, it will simply throw an exception.
recordingsDirectory = "QuiltRecordings"
try:
os.makedirs(recordingsDirectory)
except OSError as exception:
if exception.errno != errno.EEXIST: # ignore error if already existed
raise
def __init__(self,
maze_def,
reset,
video_dim=(32, 32),
num_parallel=1,
time_limit=30,
discrete_actions=False,
vision_observation=True,
depth=False,
num_frames=1,
grayscale=True):
self.video_width, self.video_height = video_dim
self.image_width, self.image_height = video_dim
self.discrete_actions = discrete_actions
self.vision_observation = vision_observation
self.depth = depth
self.num_parallel = num_parallel
maze = create_maze(maze_def)
self.mission_gen = MissionGen()
self.mission = self.mission_gen.generate_mission(
maze.create_maze_array(), reset=reset)
self.XGoalPos, self.YGoalPos = self.mission_gen.goal_pos[
0], self.mission_gen.goal_pos[2]
# with open(mission_file, 'r') as f:
# print("Loading mission from %s" % mission_file)
# mission_xml = f.read()
# self.mission = MalmoPython.MissionSpec(mission_xml, True)
self.mission.requestVideo(self.video_height, self.video_width)
self.mission.observeRecentCommands()
self.mission.allowAllContinuousMovementCommands()
# self.mission.timeLimitInSeconds(time_limit)
if self.num_parallel > 1:
self.client_pool = MalmoPython.ClientPool()
for i in range(num_parallel):
port = 10000 + i
self.client_pool.add(MalmoPython.ClientInfo("127.0.0.1", port))
self.agent_host = MalmoPython.AgentHost()
self.agent_host.setObservationsPolicy(
MalmoPython.ObservationsPolicy.KEEP_ALL_OBSERVATIONS)
# self.agent_host.setObservationsPolicy(MalmoPython.ObservationsPolicy.LATEST_OBSERVATION_ONLY)
self.agent_host.setVideoPolicy(MalmoPython.VideoPolicy.KEEP_ALL_FRAMES)
# self.agent_host.setVideoPolicy(MalmoPython.VideoPolicy.LATEST_FRAME_ONLY)
self.mission_record_spec = MalmoPython.MissionRecordSpec()
if discrete_actions:
self._action_set = {0: "move 1", 1: "turn 1", 2: "turn -1"}
self.action_space = Discrete(n=len(self._action_set))
else:
# self._action_set = ["move", "turn", "pitch"]
# self.action_space = Box(np.array([0, -.5, -.5]), np.array([1, .5, .5]))
self._action_set = [("move", (-1, 1)), ("turn", (-0.5, 0.5))]
#("jump", (-1, 1))]
lower_bound = np.asarray([x[1][0] for x in self._action_set])
upper_bound = np.asarray([x[1][1] for x in self._action_set])
self.action_space = Box(lower_bound, upper_bound)
self.num_frames = num_frames
self.grayscale = grayscale
if self.grayscale:
self.num_frame_channels = 1
high = 1
else:
self.num_frame_channels = 3
high = 255
# Obs keys and bounds
x_bounds = self.mission_gen.x_bounds
z_bounds = self.mission_gen.z_bounds
self.max_dist = np.linalg.norm((x_bounds[-1], z_bounds[-1]))
self.minDistanceFromGoal = None
if self.vision_observation:
self.observation_space = Box(
low=0,
high=high,
shape=(self.num_frames * self.num_frame_channels,
self.image_height, self.image_width))
else:
self.obs_keys = [(u'XPos', x_bounds), (u'ZPos', z_bounds),
(u'yaw', (0, 360)), (u'XGoalPos', x_bounds),
(u'YGoalPos', z_bounds),
(u'DistanceTravelled', (0, 30)),
(u'distanceFromGoal', (0, self.max_dist))]
l_bounds = [key[1][0] for key in self.obs_keys]
u_bounds = [key[1][1] for key in self.obs_keys]
self.observation_space = Box(np.array(l_bounds),
np.array(u_bounds))
# self._horizon = env.spec.timestep_limit
self.last_obs = None
self.cum_reward = 0
self.distance_travelled = 0
self.terminal = False
self.jump = 0
</RewardForDamagingEntity>
<ObservationFromNearbyEntities>
<Range name="entities" xrange="''' + str(
ARENA_WIDTH) + '''" yrange="2" zrange="''' + str(
ARENA_BREADTH) + '''" />
</ObservationFromNearbyEntities>
<ObservationFromFullStats/>''' + video_requirements + '''
</AgentHandlers>
</AgentSection>
</Mission>'''
validate = True
my_client_pool = MalmoPython.ClientPool()
my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10000))
episode_reward = 0
if agent_host.receivedArgument("test"):
num_reps = 1
else:
num_reps = 10000
fout = open('results.csv', 'w')
# Set up the agent
agent = agentMC.agentMC(agent_host, MAX_ZOMBIES, MAX_DISTANCE, 20)
for i in range(num_reps):
print('episode:', i)
for iRepeat in range(1, MAX_ZOMBIES):
#########################################
# Set up the enviornment #
#########################################
# See if we can parse our extended command line.
malmoutils.parse_command_line(agentHost)
# As we are not recording our video xml should be an empty string.
assert malmoutils.get_video_xml(agentHost) == ''
# Test that we can get a default recording spec.
assert type(malmoutils.get_default_recording_object(
agentHost, "test")) == MalmoPython.MissionRecordSpec
# Default recordings directory is ''.
assert malmoutils.get_recordings_directory(agentHost) == ''
def clientInfos(cp):
return [(c.ip_address, c.control_port, c.command_port) for c in cp.clients]
# Test adding some client infos to a client pool.
clientPool = MalmoPython.ClientPool()
assert len(clientPool.clients) == 0
c1 = ("localhost", 10000, 0)
client1 = MalmoPython.ClientInfo(*c1)
clientPool.add(client1)
assert clientInfos(clientPool) == [c1]
c2 = ("127.0.0.1", 10001, 20001)
client2 = MalmoPython.ClientInfo(*c2)
clientPool.add(client2)
assert clientInfos(clientPool) == [c1, c2]
'''
return missionXML.format(src=seedfile, limit=timelimit, xcoord=random.randint(0,300), zcoord=random.randint(100, 350), tlimit=eptime)
agent_id = 10001
counter = 9019
while counter < numphotos:
agent_host = MalmoPython.AgentHost()
try:
missionXML = generateXMLbySeed()
my_mission = MalmoPython.MissionSpec(missionXML, True)
my_mission_record = MalmoPython.MissionRecordSpec()
except Exception as e:
print("open mission ERROR: ", e)
my_clients = MalmoPython.ClientPool()
my_clients.add(MalmoPython.ClientInfo('127.0.0.1', 10000)) # add Minecraft machines here as available
agent_id += 1
# Attempt to start a mission:
max_retries = 3
for retry in range(max_retries):
try:
agent_host.startMission(my_mission, my_clients, my_mission_record, 0, "IMGCOLLECTOR")
break
except RuntimeError as e:
if retry == max_retries - 1:
print("Error starting mission:", e)
exit(1)
else:
time.sleep(2)
# Loop until mission starts:
turn_speed = self.angvel(yaw_to_next_point, yaw, 16.0)
move_speed = (1.0 - abs(turn_speed)) * (1.0 - abs(turn_speed))
self.agent_host.sendCommand("turn " + str(turn_speed))
self.agent_host.sendCommand("move " + str(move_speed))
time.sleep(0.001)
sys.stdout = os.fdopen(sys.stdout.fileno(), 'w',
0) # flush print output immediately
# Create a pool of Minecraft Mod clients.
# By default, mods will choose consecutive mission control ports, starting at 10000,
# so running four mods locally should produce the following pool by default (assuming nothing else
# is using these ports):
my_client_pool = MalmoPython.ClientPool()
my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10000))
my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10001))
my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10002))
my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10003))
my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10004))
my_client_pool.add(MalmoPython.ClientInfo("127.0.0.1", 10005))
# Create one agent host for parsing:
parser = MalmoPython.AgentHost()
options = [("nn", "n", RouteGenerators.NearestNeighbour, True),
("gen-al", "g", RouteGenerators.Genetic, False),
("div-and-conq", "d", RouteGenerators.DivideAndConquer, False),
("mst", "m", RouteGenerators.MinSpanTree, True),
("conv-hull", "c", RouteGenerators.Spiral, False),
("sa", "s", RouteGenerators.Annealing, True)]
