def cim_dqn_actor():
env = Env(**training_config["env"])
agent = MultiAgentWrapper(
{name: get_dqn_agent()
for name in env.agent_idx_list})
actor = Actor(env,
agent,
CIMTrajectoryForDQN,
trajectory_kwargs=common_config)
actor.as_worker(training_config["group"], log_dir=log_dir)
def cim_dqn_learner():
env = Env(**training_config["env"])
agent = MultiAgentWrapper(
{name: get_dqn_agent()
for name in env.agent_idx_list})
scheduler = TwoPhaseLinearParameterScheduler(
training_config["max_episode"], **training_config["exploration"])
actor = ActorProxy(
training_config["group"],
training_config["num_actors"],
update_trigger=training_config["learner_update_trigger"])
learner = OffPolicyLearner(actor, scheduler, agent,
**training_config["training"])
learner.run()
agent_id = list(state.keys())[0]
data = training_data.setdefault(agent_id,
{"args": [[] for _ in range(4)]})
data["args"][0].append(state[agent_id]) # state
data["args"][1].append(action[agent_id][0]) # action
data["args"][2].append(action[agent_id][1]) # log_p
data["args"][3].append(self.get_offline_reward(event)) # reward
for agent_id in training_data:
training_data[agent_id]["args"] = [
np.asarray(vals, dtype=np.float32 if i == 3 else None)
for i, vals in enumerate(training_data[agent_id]["args"])
]
return training_data
# Single-threaded launcher
if __name__ == "__main__":
set_seeds(1024) # for reproducibility
env = Env(**training_config["env"])
agent = MultiAgentWrapper(
{name: get_ac_agent()
for name in env.agent_idx_list})
actor = Actor(env,
agent,
CIMTrajectoryForAC,
trajectory_kwargs=common_config) # local actor
learner = OnPolicyLearner(actor, training_config["max_episode"])
learner.run()