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delay_unaware.py
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import gym
import sys
sys.path.append("/home/baiming/highway_multiagent_env")
sys.path.append("C://Users//baiming//Seafile//THUFile//Papers//highway_env_multiagent")
import highway_env
from agent import Agent
import torch
from torch import optim
import numpy as np
from tqdm import tnrange
# from utils import record_videos, capture_intermediate_frames
import argparse
import torch
import time
import os
import numpy as np
from gym.spaces import Box, Discrete
from pathlib import Path
from torch.autograd import Variable
from tensorboardX import SummaryWriter
# from utils.make_env import make_env
from utils.buffer import ReplayBuffer
# from utils.env_wrappers import SubprocVecEnv, DummyVecEnv
from algorithms.maddpg import MADDPG
USE_CUDA = True # torch.cuda.is_available()
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
def run(config):
model_dir = Path('./models')/ config.model_name
if not model_dir.exists():
curr_run = 'run1'
else:
exst_run_nums = [int(str(folder.name).split('run')[1]) for folder in
model_dir.iterdir() if
str(folder.name).startswith('run')]
if len(exst_run_nums) == 0:
curr_run = 'run1'
else:
curr_run = 'run%i' % (max(exst_run_nums) + 1)
run_dir = model_dir / curr_run
log_dir = run_dir / 'logs'
os.makedirs(log_dir)
logger = SummaryWriter(str(log_dir))
torch.manual_seed(config.seed)
np.random.seed(config.seed)
env = gym.make("intersection-multiagent-v0")
maddpg = MADDPG.init_from_env(env, agent_alg=config.agent_alg,
adversary_alg=config.adversary_alg,
tau=config.tau,
lr=config.lr,
hidden_dim=config.hidden_dim)
replay_buffer = ReplayBuffer(config.buffer_length, maddpg.nagents,
[obsp.shape[0] for obsp in env.observation_space],
[acsp.shape[0] if isinstance(acsp, Box) else acsp.n
for acsp in env.action_space])
t = 0
delay_step = config.delay_step
for ep_i in range(0, config.n_episodes, config.n_rollout_threads):
print("Episodes %i-%i of %i" % (ep_i + 1,
ep_i + 1 + config.n_rollout_threads,
config.n_episodes))
obs = env.reset()
# obs.shape = (n_rollout_threads, nagent)(nobs), nobs differs per agent so not tensor
maddpg.prep_rollouts(device='gpu')
explr_pct_remaining = max(0, config.n_exploration_eps - ep_i) / config.n_exploration_eps
maddpg.scale_noise(config.final_noise_scale + (config.init_noise_scale - config.final_noise_scale) * explr_pct_remaining)
maddpg.reset_noise()
agent_obs = []
for i in range(4):
agent_obs.append(np.array([obs[i%4], obs[(i+1)%4], obs[(i+2)%4], obs[(i+3)%4]]).flatten())
obs = np.array([agent_obs])
zero_agent_actions = [1,1,1,1]
last_agent_actions = [zero_agent_actions for _ in range(delay_step)]
for et_i in range(config.episode_length):
# rearrange observations to be per agent, and convert to torch Variable
torch_obs = [torch.FloatTensor(np.vstack(obs[:, i])) for i in range(maddpg.nagents)]
# get actions as torch Variables
# print(obs)
torch_agent_actions = maddpg.step(torch_obs, explore=True)
# convert actions to numpy arrays
agent_actions = [ac.data.numpy() for ac in torch_agent_actions]
# print(agent_actions)
# rearrange actions to be per environment
if delay_step == 0:
actions = [np.argmax(agent_actions[i][0]) for i in range(4)]
else:
future_actions = [np.argmax(agent_actions[i][0]) for i in range(4)]
actions = last_agent_actions[0]
last_agent_actions = last_agent_actions[1:]
last_agent_actions.append(future_actions)
next_obs, rewards, dones, infos = env.step(actions)
# print(rewards)
replay_buffer.push(obs, agent_actions, rewards, next_obs, dones)
if dones[0][0]:
break
obs = next_obs
t += config.n_rollout_threads
if (len(replay_buffer) >= config.batch_size and
(t % config.steps_per_update) < config.n_rollout_threads):
if USE_CUDA:
maddpg.prep_training(device='gpu')
else:
maddpg.prep_training(device='cpu')
for u_i in range(config.n_rollout_threads):
for a_i in range(maddpg.nagents): #do not update the runner
sample = replay_buffer.sample(config.batch_size,
to_gpu=USE_CUDA)
maddpg.update(sample, a_i, logger=logger)
maddpg.update_all_targets()
maddpg.prep_rollouts(device='gpu')
ep_rews = replay_buffer.get_average_rewards(
config.episode_length * config.n_rollout_threads)
for a_i, a_ep_rew in enumerate(ep_rews):
# logger.add_scalar('agent%i/mean_episode_rewards' % a_i, a_ep_rew, ep_i)
logger.add_scalars('agent%i/mean_episode_rewards' % a_i, {'reward': a_ep_rew}, ep_i)
if ep_i % config.save_interval < config.n_rollout_threads:
os.makedirs(run_dir / 'incremental', exist_ok=True)
maddpg.save(run_dir / 'incremental' / ('model_ep%i.pt' % (ep_i + 1)))
maddpg.save(run_dir / 'model.pt')
maddpg.save(run_dir / 'model.pt')
env.close()
logger.export_scalars_to_json(str(log_dir / 'summary.json'))
logger.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("model_name",
help="Name of directory to store " +
"model/training contents")
parser.add_argument("--seed",
default=1, type=int,
help="Random seed")
parser.add_argument("--n_rollout_threads", default=1, type=int)
parser.add_argument("--n_training_threads", default=6, type=int)
parser.add_argument("--buffer_length", default=int(1e7), type=int)
parser.add_argument("--n_episodes", default=20000, type=int)
parser.add_argument("--episode_length", default=100, type=int) #25
parser.add_argument("--steps_per_update", default=100, type=int)
parser.add_argument("--delay_step", default=1, type=int)
parser.add_argument("--batch_size",
default=1024, type=int,
help="Batch size for model training")
parser.add_argument("--n_exploration_eps", default=2000, type=int)
parser.add_argument("--init_noise_scale", default=0.3, type=float)
parser.add_argument("--final_noise_scale", default=0.0, type=float)
parser.add_argument("--save_interval", default=1000, type=int)
parser.add_argument("--hidden_dim", default=64, type=int)
parser.add_argument("--lr", default=0.01, type=float)
parser.add_argument("--tau", default=0.01, type=float)
parser.add_argument("--agent_alg",
default="MADDPG", type=str,
choices=['MADDPG', 'DDPG'])
parser.add_argument("--adversary_alg",
default="MADDPG", type=str,
choices=['MADDPG', 'DDPG'])
parser.add_argument("--discrete_action",
action='store_true')
config = parser.parse_args()
run(config)