def main():
"""
Demo how to run an agent
"""
dirname = os.path.dirname(__file__)
if (len(sys.argv) > 1):
env_configName = str(sys.argv[1])
agent_configName = str(sys.argv[2])
else:
print("Default config ")
env_configName = "./configs/factory_floor_experiment.yaml"
env_filename = os.path.join(dirname, env_configName)
agent_configName = "./configs/agent_combined_config.yaml"
agent_filename = os.path.join(dirname, agent_configName)
env_parameters = getParameters(env_filename)
agent_parameters = getParameters(agent_filename)
# whao, you need to know exact contents of all files here..
recursive_update(
agent_parameters['subAgentList'][0]['parameters']['simulator'],
env_parameters['environment'])
print(env_parameters)
print(agent_parameters)
random.seed(env_parameters['seed'])
maxSteps = env_parameters['max_steps']
baseEnv = FactoryFloor(env_parameters['environment'])
packedActionSpace = PackedSpace(baseEnv.action_space,
{"robots": ["robot1", "robot2", "robot3"]})
env = ModifiedGymEnv(baseEnv, packedActionSpace)
logging.info("Starting example MCTS agent")
logoutput = io.StringIO("episode output log")
try:
logoutputpickle = open('./' + os.environ["SLURM_JOB_ID"] + '.pickle',
'wb')
except KeyError:
print("No SLURM_JOB_ID found")
logoutputpickle = io.BytesIO()
obs = env.reset()
complexAgent = createAgent(env.action_space, env.observation_space,
agent_parameters)
experiment = Experiment(complexAgent, env, maxSteps, render=True)
experiment.addListener(JsonLogger(logoutput))
experiment.addListener(PickleLogger(logoutputpickle))
stats, confidence_ints = experiment.run()
logoutputpickle.close()
print("json output:", logoutput.getvalue())
print("\n\nREWARD STATS: " + str(stats) + " \nCONFIDENCE INTERVALS " +
str(confidence_ints))
def main():
"""
Demo how to run an agent
"""
if( len(sys.argv) > 1 ):
configName = str(sys.argv[1])
filename = configName
else:
print("Default config ")
configName = "./configs/new_traffic_loop_ppo.yaml"
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname, configName)
print( "Config name " + configName )
logging.info("Starting example PPO agent")
logoutput = io.StringIO("episode output log")
parameters = getParameters(filename)
env = SumoGymAdapter(parameters['all'])
# here we initialize all agents (in that case one)
PPOAgents = []
env.reset()
for intersectionId in env.action_space.spaces.keys():
PPOAgents.append(PPOAgent(agentId=intersectionId, environment=env, parameters=parameters['all']))
complexAgent = BasicComplexAgent(PPOAgents)
experiment = Experiment(complexAgent, env, parameters['all']['max_steps'], parameters['all']['seedlist'], render=False)
experiment.addListener(JsonLogger(logoutput))
experiment.run()
def test_FactoryFloorAgent(self):
logging.info("Starting test factoryfloor agent")
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname,
"../configs/factory_floor_simple.yaml")
parameters = getParameters(filename)
env = FactoryFloor(parameters)
randomAgents = []
for robotId in env.action_space.spaces.keys():
randomAgents.append(
FactoryFloorAgent(robotId, env.action_space,
env.observation_space, parameters))
complexAgent = BasicComplexAgent(randomAgents, env.action_space,
env.observation_space)
Experiment(complexAgent, env, 1000, None, True).run()
def main():
"""
Demo how to construct complex agents
"""
configName = "configs/factory_floor_complex.yaml"
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname, configName)
parameters = getParameters(filename)
env = FactoryFloor(parameters['environment'])
obs = env.reset()
complexAgent = createAgent(env.action_space, env.observation_space,
parameters['agents'])
print(complexAgent)
sub = complexAgent._agentSubcomponents[0]
subsub = sub._agentSubcomponents
print(subsub)
def est_PPO_agent(self):
logging.info("Starting test PPO agent")
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname,
"../configs/factory_floor_simple.yaml")
parameters = getParameters(filename)
env = FactoryFloor(parameters)
PPOAgents = []
for robotId in env.action_space.spaces.keys():
PPOAgents.append(
PPOAgent(parameters, env.observation_space,
env.action_space.spaces.get(robotId), robotId))
complexAgent = BasicComplexAgent(PPOAgents)
steps = 0
while steps < parameters["max_steps"]:
steps += Episode(complexAgent, env,
env.action_space.sample()).run()
def test_mcts_agent(self):
"""
Really a smoketest/demo how to run an agent
"""
logging.info("Starting test_mcts_agent")
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname,
"../configs/factory_floor_simple.yaml")
parameters = getParameters(filename)
env = FactoryFloor(parameters)
obs = env.reset()
mctsAgents = []
randomagent = 'aiagents.single.RandomAgent.RandomAgent'
for robotId in env.action_space.spaces.keys():
mctsparams = {
'treeAgent': {
'class': randomagent,
'id': robotId,
'parameters': {}
},
'rolloutAgent': {
'class': randomagent,
'id': robotId,
'parameters': {}
}
}
mctsparams['simulator'] = dict(parameters)
mctsparams['simulator'][
'fullname'] = "aienvs.FactoryFloor.FactoryFloor.FactoryFloor"
mctsAgents.append(
MctsAgent(robotId, env.action_space, env.observation_space,
mctsparams))
complexAgent = BasicComplexAgent(mctsAgents, env.action_space,
env.observation_space)
episode = Episode(complexAgent, env, obs, render=True)
episode.run()
def main():
if (len(sys.argv) > 1):
configName = str(sys.argv[1])
filename = configName
else:
print("Default config ")
configName = "../configs/PPO.yaml"
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname, configName)
logging.info("Starting example warehouse DQN agent")
logoutput = io.StringIO("episode output log")
parameters = getParameters(filename)
env = Warehouse()
# here we initialize all agents (in that case one)
agents = []
env.reset()
for agent_id, action_space in env.action_space.spaces.items():
agents.append(
WarehouseNaiveAgent(robot_id=agent_id,
actionspace=action_space,
observationspace=env.observation_space))
breakpoint()
agents[20] = PPOAgent(agentId=20,
actionspace=env.action_space[20],
observationspace=env.observation_space,
parameters=parameters['all'])
multi_agent = MultiAgent(agents)
experiment = Experiment(
multi_agent,
env,
maxSteps=1e6,
render=True,
seedlist=[random.randrange(1, 1e3, 1) for i in range(int(1e5))],
renderDelay=0.5)
experiment.addListener(JsonLogger(logoutput))
experiment.run()
def main():
"""
MCTS Factory Floor experiment
"""
dirname = os.path.dirname(__file__)
parser = configargparse.ArgParser()
parser.add('-e',
'--env-config',
dest="env_filename",
default=os.path.join(
dirname, "./debug_configs/factory_floor_experiment.yaml"))
parser.add('-a',
'--agent-config',
dest="agent_filename",
default=os.path.join(dirname,
"./debug_configs/agent_config.yaml"))
parser.add('-d', '--data-dirname', dest="data_dirname", default="data")
args = parser.parse_args()
try:
data_outputdir = os.path.join(
dirname,
"./" + args.data_dirname + "/" + os.environ["SLURM_JOB_ID"])
os.makedirs(data_outputdir)
logoutputpickle = open('./' + data_outputdir + '/output.pickle', 'wb')
rewardsFile = open('./' + data_outputdir + '/rewards.yaml', 'w+')
except KeyError:
print("No SLURM_JOB_ID found")
logoutputpickle = io.BytesIO()
rewardsFile = io.StringIO()
env_parameters = getParameters(args.env_filename)
agent_parameters = getParameters(args.agent_filename)
print(env_parameters)
print(agent_parameters)
for subAgent in agent_parameters["subAgentList"]:
subAgent["parameters"]["simulator"] = env_parameters["environment"]
subAgent["parameters"]["simulator"][
"fullname"] = "aienvs.FactoryFloor.FactoryFloor.FactoryFloor"
random.seed(env_parameters['seed'])
maxSteps = env_parameters['max_steps']
env = FactoryFloor(env_parameters['environment'])
logging.info("Starting example MCTS agent")
logoutput = io.StringIO("episode output log")
obs = env.reset()
complexAgent = createAgent(env.action_space, env.observation_space,
agent_parameters)
experiment = Experiment(complexAgent, env, maxSteps, render=False)
experiment.addListener(JsonLogger(logoutput))
experiment.addListener(PickleLogger(logoutputpickle))
rewards = experiment.run()
# statistics, confidence_ints = stats.describe(rewards), stats.bayes_mvs(rewards)
logoutputpickle.close()
yaml.dump(rewards, rewardsFile)
rewardsFile.close()
print("json output:", logoutput.getvalue())
print("\n\nREWARD STATS: " + str(statistics) + " \nCONFIDENCE INTERVALS " +
str(confidence_ints))