def launch(config):
config = convert_dottable(config)
env = Env(config.env.scenario,
config.env.topology,
durations=config.env.durations)
agent_id_list = [str(agent_id) for agent_id in env.agent_idx_list]
config["agents"]["input_dim"] = CIMStateShaper(
**config.env.state_shaping).dim
agent_manager = POAgentManager(name="cim_learner",
mode=AgentManagerMode.TRAIN,
agent_dict=create_po_agents(
agent_id_list, config.agents))
proxy_params = {
"group_name": os.environ["GROUP"],
"expected_peers": {
"actor": int(os.environ["NUM_ACTORS"])
},
"redis_address": ("localhost", 6379)
}
learner = SimpleLearner(
agent_manager=agent_manager,
actor=ActorProxy(proxy_params=proxy_params,
experience_collecting_func=
merge_experiences_with_trajectory_boundaries),
scheduler=Scheduler(config.main_loop.max_episode),
logger=Logger("cim_learner", auto_timestamp=False))
learner.learn()
learner.test()
learner.dump_models(os.path.join(os.getcwd(), "models"))
learner.exit()
def launch(config):
config = convert_dottable(config)
# Step 1: Initialize a CIM environment for using a toy dataset.
env = Env(config.env.scenario, config.env.topology, durations=config.env.durations)
agent_id_list = [str(agent_id) for agent_id in env.agent_idx_list]
action_space = list(np.linspace(-1.0, 1.0, config.agents.algorithm.num_actions))
# Step 2: Create state, action and experience shapers. We also need to create an explorer here due to the
# greedy nature of the DQN algorithm.
state_shaper = CIMStateShaper(**config.env.state_shaping)
action_shaper = CIMActionShaper(action_space=action_space)
experience_shaper = TruncatedExperienceShaper(**config.env.experience_shaping)
# Step 3: Create agents and an agent manager.
config["agents"]["algorithm"]["input_dim"] = state_shaper.dim
agent_manager = DQNAgentManager(
name="cim_learner",
mode=AgentManagerMode.TRAIN_INFERENCE,
agent_dict=create_dqn_agents(agent_id_list, config.agents),
state_shaper=state_shaper,
action_shaper=action_shaper,
experience_shaper=experience_shaper
)
# Step 4: Create an actor and a learner to start the training process.
scheduler = TwoPhaseLinearParameterScheduler(config.main_loop.max_episode, **config.main_loop.exploration)
actor = SimpleActor(env, agent_manager)
learner = SimpleLearner(
agent_manager, actor, scheduler,
logger=Logger("cim_learner", format_=LogFormat.simple, auto_timestamp=False)
)
learner.learn()
learner.test()
learner.dump_models(os.path.join(os.getcwd(), "models"))
def launch(config, distributed_config):
config = convert_dottable(config)
distributed_config = convert_dottable(distributed_config)
env = Env(config.env.scenario,
config.env.topology,
durations=config.env.durations)
agent_id_list = [str(agent_id) for agent_id in env.agent_idx_list]
config["agents"]["algorithm"]["input_dim"] = CIMStateShaper(
**config.env.state_shaping).dim
agent_manager = DQNAgentManager(name="cim_learner",
mode=AgentManagerMode.TRAIN,
agent_dict=create_dqn_agents(
agent_id_list, config.agents))
proxy_params = {
"group_name":
os.environ["GROUP"]
if "GROUP" in os.environ else distributed_config.group,
"expected_peers": {
"actor":
int(os.environ["NUM_ACTORS"] if "NUM_ACTORS" in
os.environ else distributed_config.num_actors)
},
"redis_address":
(distributed_config.redis.hostname, distributed_config.redis.port),
"max_retries":
15
}
learner = SimpleLearner(
agent_manager=agent_manager,
actor=ActorProxy(
proxy_params=proxy_params,
experience_collecting_func=concat_experiences_by_agent),
scheduler=TwoPhaseLinearParameterScheduler(
config.main_loop.max_episode, **config.main_loop.exploration),
logger=Logger("cim_learner", auto_timestamp=False))
learner.learn()
learner.test()
learner.dump_models(os.path.join(os.getcwd(), "models"))
learner.exit()
from maro.simulator import Env
from maro.utils import Logger
from components.agent_manager import DQNAgentManager
from components.config import config
from components.state_shaper import CIMStateShaper
if __name__ == "__main__":
env = Env(config.env.scenario, config.env.topology, durations=config.env.durations)
agent_id_list = [str(agent_id) for agent_id in env.agent_idx_list]
state_shaper = CIMStateShaper(**config.state_shaping)
exploration_config = {"epsilon_range_dict": {"_all_": config.exploration.epsilon_range},
"split_point_dict": {"_all_": config.exploration.split_point},
"with_cache": config.exploration.with_cache
}
explorer = TwoPhaseLinearExplorer(agent_id_list, config.general.total_training_episodes, **exploration_config)
agent_manager = DQNAgentManager(name="cim_remote_learner", agent_id_list=agent_id_list, mode=AgentMode.TRAIN,
state_shaper=state_shaper, explorer=explorer)
proxy_params = {"group_name": config.distributed.group_name,
"expected_peers": config.distributed.learner.peer,
"redis_address": (config.distributed.redis.host_name, config.distributed.redis.port)
}
learner = SimpleLearner(trainable_agents=agent_manager,
actor=ActorProxy(proxy_params=proxy_params),
logger=Logger("distributed_cim_learner", auto_timestamp=False))
learner.train(total_episodes=config.general.total_training_episodes)
learner.test()
learner.dump_models(os.path.join(os.getcwd(), "models"))