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train_algs.py
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# import supersuit
import wandb
import torch as th
from wandb.integration.sb3 import WandbCallback
import os
from stable_baselines3 import PPO, A2C, TD3, SAC, DQN, HerReplayBuffer, HER, DDPG
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.env_util import make_vec_env
from sb3_contrib import TQC, MaskablePPO, TRPO, ARS, RecurrentPPO, QRDQN
# pip install git+https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
from uav_env import parallel_env
from uav_centralized_env import DronesEnv as single_agent_env
from result_buffer import ResultBuffer
from supersuit import pettingzoo_env_to_vec_env_v1, concat_vec_envs_v1
from input_config import InputConfig
os.environ["WANDB_SILENT"] = "True"
project_name = "multi-agent-uav-offloading-FITCE"
print("starting {} project".format(project_name))
eval_episodes = 20 # 250
p = 1.75
o = 1
m_algo = [DQN, QRDQN, A2C, PPO, TRPO, ARS] # multi agent
s_algo = [A2C, PPO, TRPO]
n_uav = [4, 6, 8]
for n_uavs in n_uav:
for single_agents in [True, False]:
if single_agents:
algo = s_algo
else:
algo = m_algo
for alg in algo:
res_buffer = ResultBuffer(min_n_drone=n_uavs, max_n_drone=n_uavs, min_mu=p, max_mu=p, step_mu=0.1,
net_slice=1, change_processing=True, alg=alg.__name__)
config = {"algo": alg.__name__,
"n_cpus": 1,
"uavs": n_uavs,
"frame_stack": 4,
"processing_rate": p,
"offloading_rate": o, # 2.5
"transition_probability_low": 1 / 180,
"transition_probability_high": 1 / 60,
"shifting_probs": True, # True gives better learning curve, while False gives nice results quicker
"lambda_low": 1.3,
"lambda_high": 2.3,
"policy_type": "MlpPolicy",
"total_timesteps": 500000, # 500000 # 1000000
"single_agent": single_agents,
"training": True,
}
input_config = InputConfig(uavs=config["uavs"],
frame_stack=config["frame_stack"],
processing_rate=config["processing_rate"],
offloading_rate=config["offloading_rate"],
lmbda=[config["lambda_low"], config["lambda_high"]],
prob_trans=[config["transition_probability_low"],
config["transition_probability_high"]],
shifting_probs=config["shifting_probs"],
algorithm=alg,
)
input_config.print_settings()
if single_agents:
uav_env = single_agent_env(input_c=input_config)
uav_env = concat_vec_envs_v1(uav_env, 1, num_cpus=config["n_cpus"],
base_class='stable_baselines3')
res_buffer.set_save_runs(n_drones=config["uavs"], mu=config["processing_rate"])
eval_env = single_agent_env(input_c=input_config, result_buffer=res_buffer)
eval_env = concat_vec_envs_v1(eval_env, 1, num_cpus=config["n_cpus"],
base_class='stable_baselines3')
else:
uav_env = parallel_env(input_c=input_config)
uav_env = pettingzoo_env_to_vec_env_v1(uav_env)
uav_env = concat_vec_envs_v1(uav_env, 1, num_cpus=config["n_cpus"],
base_class='stable_baselines3')
res_buffer.set_save_runs(n_drones=config["uavs"], mu=config["processing_rate"])
eval_env = parallel_env(input_c=input_config, result_buffer=res_buffer)
eval_env = pettingzoo_env_to_vec_env_v1(eval_env)
eval_env = concat_vec_envs_v1(eval_env, 1, num_cpus=config["n_cpus"],
base_class='stable_baselines3')
# uav_env = supersuit.normalize_obs_v0(uav_env, env_min=0, env_max=1)
# uav_env = supersuit.frame_stack_v1(uav_env, 3)
# need to initialize it even for static policies to use evaluate_policy from sb3
if alg != ARS:
model = alg(config["policy_type"], uav_env, verbose=0, gamma=0.95, tensorboard_log=f"runs")
else:
model = alg(config["policy_type"], uav_env, verbose=0, tensorboard_log=f"runs")
for i in range(3):
config["training"] = True
run = wandb.init(
project=project_name,
tags=["n {}".format(config["uavs"]),
"pr {}".format(config["processing_rate"]),
"or {}".format(config["offloading_rate"]),
"lmbda_l {:.2f}, lmbda_h {:.2f}".format(config["lambda_low"], config["lambda_high"]),
"prob_l {:.2f}, prob_h {:.2f}".format(config["transition_probability_low"],
config["transition_probability_high"]),
"alg {}".format(alg.__name__),
"single-agent:{}".format(single_agents)
],
entity="xraulz",
reinit=True,
sync_tensorboard=True,
config=config,
)
wandb.run.name = wandb.run.name + "-{}-tr".format(alg.__name__)
wandb.run.save()
if os.name == 'nt':
model.learn(
total_timesteps=config["total_timesteps"],
)
else:
model.learn(
total_timesteps=config["total_timesteps"],
callback=WandbCallback(
gradient_save_freq=100,
model_save_path=f"models/{project_name}/{run.name}",
verbose=2,
),
)
# model is already saved via model_save_path inside model.learn
# model.save("policy {}-{}".format(run.name, i))
run.finish()
print("initializing evaluation...")
config["training"] = False
run = wandb.init(
project=project_name,
tags=["n {}".format(config["uavs"]),
"pr {}".format(config["processing_rate"]),
"or {}".format(config["offloading_rate"]),
"lmbda_l {:.2f}, lmbda_h {:.2f}".format(config["lambda_low"], config["lambda_high"]),
"prob_l {:.2f}, prob_h {:.2f}".format(config["transition_probability_low"],
config["transition_probability_high"]),
"evaluation",
"alg {}".format(alg.__name__),
],
entity="xraulz",
reinit=True,
sync_tensorboard=True,
config=config,
)
wandb.run.name = wandb.run.name + "-{}-eval".format(alg.__name__)
wandb.run.save()
ev_episodes = eval_episodes if single_agents else eval_episodes * n_uavs
mean_r, std_r = evaluate_policy(model, eval_env, n_eval_episodes=ev_episodes, deterministic=True)
run.finish()