def main(args):
# Initialize the environment
env = magent.GridWorld('battle', map_size=args.map_size)
env.set_render_dir(
os.path.join(BASE_DIR, 'examples/battle_model', 'build/render'))
handles = env.get_handles()
tf_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
tf_config.gpu_options.allow_growth = True
log_dir = os.path.join(BASE_DIR, 'data/tmp'.format(args.algo))
model_dir = os.path.join(BASE_DIR, 'data/models/{}'.format(args.algo))
start_from = 0
sess = tf.Session(config=tf_config)
models = [
spawn_ai(args.algo, sess, env, handles[0], args.algo + '-me',
args.max_steps),
spawn_ai(args.algo, sess, env, handles[1], args.algo + '-opponent',
args.max_steps)
]
sess.run(tf.global_variables_initializer())
runner = tools.Runner(sess,
env,
handles,
args.map_size,
args.max_steps,
models,
play,
render_every=args.save_every if args.render else 0,
save_every=args.save_every,
tau=0.01,
log_name=args.algo,
log_dir=log_dir,
model_dir=model_dir,
train=True)
for k in range(start_from, start_from + args.n_round):
eps = linear_decay(k, [0, int(args.n_round * 0.8), args.n_round],
[1, 0.2, 0.1])
runner.run(eps, k)
original_main = False
#
if args.oppo in ['ac', 'il', 'mfq', 'mfac']:
original_oppo = True
else:
original_oppo = False
#
if args.neighbor_nums == -1:
bias_flag = False
else:
bias_flag = True
sess = tf.Session(config=tf_config)
#
if args.algo == args.oppo:
models = [
spawn_ai(args.algo, sess, env, handles[0], args.algo + '-me',
args.max_steps, args.neighbor_nums, bias_flag),
spawn_ai(args.oppo, sess, env, handles[1], args.oppo + '-opponent',
args.max_steps, args.neighbor_nums, bias_flag)
]
else:
models = [
spawn_ai(args.algo, sess, env, handles[0], args.algo + '-me',
args.max_steps, args.neighbor_nums, bias_flag),
spawn_ai(args.oppo, sess, env, handles[1], args.oppo + '-me',
args.max_steps, args.neighbor_nums, bias_flag)
]
#
sess.run(tf.global_variables_initializer())
env.set_render_dir(
os.path.join(BASE_DIR, 'examples/battle_model', 'build/render'))
handles = env.get_handles()
tf_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
tf_config.gpu_options.allow_growth = True
main_model_dir = os.path.join(BASE_DIR,
'data/models/{}-0'.format(args.algo))
oppo_model_dir = os.path.join(BASE_DIR,
'data/models/{}-1'.format(args.oppo))
sess = tf.Session(config=tf_config)
models = [
spawn_ai(args.algo, sess, env, handles[0], args.algo + '-me',
args.max_steps),
spawn_ai(args.oppo, sess, env, handles[1], args.oppo + '-opponent',
args.max_steps)
]
sess.run(tf.global_variables_initializer())
models[0].load(main_model_dir, step=args.idx[0])
models[1].load(oppo_model_dir, step=args.idx[1])
runner = tools.Runner(sess,
env,
handles,
args.map_size,
args.max_steps,
models,
battle,
# Initialize the environment
env = magent.GridWorld('battle', map_size=args.map_size)
env.set_render_dir(os.path.join(BASE_DIR, 'examples/battle_model', 'build/render'))
handles = env.get_handles()
mtmfq_position = args.mtmfqp
tf_config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
tf_config.gpu_options.allow_growth = True
main_model_dir = os.path.join(BASE_DIR, 'data/models/{}-0'.format(args.algo))
oppo_model_dir1 = os.path.join(BASE_DIR, 'data/models/{}-1'.format(args.oppo1))
oppo_model_dir2 = os.path.join(BASE_DIR, 'data/models/{}-2'.format(args.oppo2))
oppo_model_dir3 = os.path.join(BASE_DIR, 'data/models/{}-3'.format(args.oppo3))
sess = tf.Session(config=tf_config)
models = [spawn_ai(args.algo, sess, env, handles[0], args.algo + '-me', args.max_steps), spawn_ai(args.oppo1, sess, env, handles[1], args.oppo1 + '-opponent1', args.max_steps), spawn_ai(args.oppo2, sess, env, handles[2], args.oppo2 + '-opponent2', args.max_steps), spawn_ai(args.oppo3, sess, env, handles[3], args.oppo3 + '-opponent3', args.max_steps)]
sess.run(tf.global_variables_initializer())
models[0].load(main_model_dir, step=args.idx[0])
models[1].load(oppo_model_dir1, step=args.idx[1])
models[2].load(oppo_model_dir2, step=args.idx[2])
models[3].load(oppo_model_dir3, step=args.idx[3])
runner = tools.Runner(sess, env, handles, args.map_size, args.max_steps, models, battle, mtmfq_position, render_every=0)
win_cnt = {'main': 0, 'opponent1': 0, 'opponent2': 0, 'opponent3': 0}
total_rewards = []
with open('storepoints_multibattle.csv', 'w+') as myfile:
myfile.write('{0},{1},{2},{3},{4}\n'.format("Game", "Reward 1", "Reward 2","Reward 3", "Reward 4"))
for k in range(0, args.n_round):
total_rewards = runner.run(0.0, k, win_cnt=win_cnt)
with open('storepoints_multibattle.csv', 'a') as myfile:
model_dir = os.path.join(BASE_DIR, 'data/models/{}'.format(args.algo))
with open('predator.csv', 'w+') as myfile:
myfile.write('{0},{1}\n'.format("Episode", "Reward"))
if args.algo in ['mfq', 'mfac', 'mtmfq']:
use_mf = True
else:
use_mf = False
start_from = 0
total_reward = []
meanerrortotal = []
sess = tf.Session(config=tf_config)
models = [
spawn_ai(args.algo, sess, env, handles[0], args.algo + '-me',
args.max_steps),
spawn_ai(args.algo, sess, env, handles[1], args.algo + '-opponent1',
args.max_steps),
spawn_ai(args.algo, sess, env, handles[2], args.algo + '-opponent2',
args.max_steps),
spawn_ai(args.algo, sess, env, handles[3], args.algo + '-opponent3',
args.max_steps)
]
sess.run(tf.global_variables_initializer())
if args.algo == 'mtmfq':
runner = tools.Runner(
sess,
env,
handles,
args.map_size,
args.max_steps,
start_from = 0
sess = tf.Session(config=tf_config)
main_model_dir = os.path.join(BASE_DIR,
'data/models/{}-0'.format(args.algo))
oppo_model_dir = os.path.join(BASE_DIR,
'data/models/{}-1'.format(args.algo))
main_msg_dir = os.path.join(BASE_DIR,
'data/models/{}-msg0'.format(args.algo))
oppo_msg_dir = os.path.join(BASE_DIR,
'data/models/{}-msg1'.format(args.algo))
models = [
spawn_ai(args.algo, sess, env, handles[0], args.algo + '-me',
args.max_steps),
spawn_ai(args.algo, sess, env, handles[1], args.algo + '-oppo',
args.max_steps)
]
if args.usemsg != 'None':
MsgModels = [
spawn_ai('msgdqn', sess, env, handles[0], 'msgdqn' + '-me',
args.max_steps),
spawn_ai('msgdqn', sess, env, handles[1], 'msgdqn' + '-opponent',
args.max_steps)
]
else:
print('do not use msg models')
MsgModels = [None, None]
sess.run(tf.global_variables_initializer())
log_dir = os.path.join(BASE_DIR,'data/tmp'.format(args.algo))
model_dir = os.path.join(BASE_DIR, 'data/models/{}'.format(args.algo))
with open('gather.csv', 'w+') as myfile:
myfile.write('{0},{1}\n'.format("Episode", "Reward"))
if args.algo in ['mfq', 'mfac', 'mtmfq']:
use_mf = True
else:
use_mf = False
start_from = 0
total_reward = []
sess = tf.Session(config=tf_config)
models = [spawn_ai(args.algo, sess, env, player_handles[0], args.algo + '-me', args.max_steps), spawn_ai(args.algo, sess, env, player_handles[1], args.algo + '-opponent1', args.max_steps), spawn_ai(args.algo, sess, env, player_handles[2], args.algo + '-opponent2', args.max_steps), spawn_ai(args.algo, sess, env, player_handles[3], args.algo + '-opponent3', args.max_steps)]
sess.run(tf.global_variables_initializer())
if args.algo == 'mtmfq':
runner = tools.Runner(sess, env, handles, args.map_size, args.max_steps, models, play2,
render_every=args.save_every if args.render else 0, save_every=args.save_every, tau=0.01, log_name=args.algo,
log_dir=log_dir, model_dir=model_dir, train=True)
else:
runner = tools.Runner(sess, env, handles, args.map_size, args.max_steps, models, play,
render_every=args.save_every if args.render else 0, save_every=args.save_every, tau=0.01, log_name=args.algo,
log_dir=log_dir, model_dir=model_dir, train=True)
for k in range(start_from, start_from + args.n_round):
eps = linear_decay(k, [0, int(args.n_round * 0.8), args.n_round], [1, 0.2, 0.1])
total_reward = runner.run(eps, k)