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config.py
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config.py
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import time
import numpy as np
from UTIL.colorful import *
from UTIL.config_args import ChainVar
'''
GlobalConfig: This config class will be 'injected' with new settings from JSONC.
(E.g., override configs with ```python main.py --cfg example.jsonc```)
(As the name indicated, ChainVars will change WITH vars it 'chained_with' during config injection)
(please see UTIL.config_args to find out how this advanced trick works out.)
* Explaining a very important setting option:
- align_episode (True/False):
In align mode, all threads begin new episode synchronously,
which means a env thread that ends early has to 'wait' for other threads before restarting
If set to 'False', threads will not wait for each others, and will 'reset' immediately on 'done'
- note (str):
Name you experiment carefully with note setting.
The note defines where the results of a single experiment will go.
for example, if note='conc', everything produced in the experiment will be save in RESULT/conc/*,
including images, saved pytorch model,
- env_name:
Which mission/environment/task to use,
See ./MISSION/env_router.py for the dictionary of available envs.
- env_path:
The path of selected mission. In fact, hmp do not need this setting at all,
it exists here only to Double Check that you have chosen the correct mission env.
* Why the Algorithm selection is not here ?!
It is the missions (envs) that choose the algorithm(s)! Please go to mission configuration!
Important to remember:
- hmp selects a mission,
- mission selects algorithm(s).
In fact, if you have two teams in env,
you can choose two different algorithms to fight each other in the same env!
- Please goto ./MISSION/env_router.py to find out where the ScenarioConfig of your env is written,
- Please set ```TEAM_NAMES``` to include the path of your favored algorithm(s)
'''
class GlobalConfig(object): # ADD_TO_CONF_SYSTEM //DO NOT remove this comment//
align_episode = True # ! please try to understand this with TOP priority
env_name = 'sr_tasks->cargo' # which environment, see ./MISSION/env_router.py
env_path = 'MISSION.sr_tasks.multiagent.cargo' # path of environment, double check to prevent mistake
draw_mode = 'OFF' # 'Img','Threejs','Web','Native'
activate_logger = True # activate data plotting (Tensorboard is not used because I do not like it)
data_logger = 'auto load, do not change this var!' # activate data plotting (Tensorboard is not used because I do not like it)
resume_mod = False # resume unfinished
mt_act_order = 'old_method' # resume unfinished
mt_parallel = False # resume unfinished
seed = np.random.randint(0, 100000) # seed for numpy and pytorch
# ! warning, the note also determine where the experiment log is stored, typically at ./RESULT/$note/*
note = 'more_testing' # in case you forget the purpose of this trainning session, write a note
logdir = './RESULT/%s/'%note
logdir_cv = ChainVar(lambda note: './RESULT/%s/'%note, chained_with=['note'])
recall_previous_session = False # continue previously interrupted training session
test_only = False # only testing and no training, it controlls a flag sending to Alg side
test_logger = 'test_only_profile.txt' # logger path, experimental, writing win rate in a file
device = 'cuda' # choose from 'cpu' (no GPU), 'cuda' (auto select GPU), 'cuda:3' (manual select GPU)
gpu_party = 'off' # GPU memory is precious! assign multiple training process to a 'party', they will share GPU memory
manual_gpu_ctl = False # auto variable, do not change!
gpu_fraction = 1.0 # maximum GPU memory usage percent, e.g., 0.5 means using half GPU memory
num_threads = 64 # run N parallel envs, a 'env' is refered to as a 'thread'
fold = 1 # A 'linux process' can handle multiple envs ('thread'), run N parallel envs, on (N//fold) processes
# this 'folding' is designed for IPC efficiency, you can thank python GIL for such a strange design...
n_parallel_frame = int(5e6) # Number of frames to run (in each frame, all parallel-envs step once)
max_n_episode = int(2e5) # max number of episodes
use_float64 = False # force float64 when converting numpy->tensor
interested_team = 0 # the interested agents, used in reward recording
interested_agent_num = 50 # the interested agents, used in reward recording
interested_agent_uid = range(0,50) # the indices of interested agents, used in reward recording
interested_agent_uid_cv = ChainVar(lambda interested_agent_num:range(0,interested_agent_num), chained_with=['interested_agent_num'])
report_reward_interval = num_threads # reporting interval
report_reward_interval_cv = ChainVar(lambda num_threads:num_threads, chained_with=['num_threads'])
train_time_testing = True # allow hmp to test algorithm policies every test_interval episodes
test_interval = 32*num_threads # test interval
test_interval_cv = ChainVar(lambda num_threads:4*num_threads, chained_with=['num_threads'])
test_epoch = 32 if num_threads <= 32 else num_threads # test epoch
test_epoch_cv = ChainVar(lambda num_threads: 32 if num_threads <= 32 else num_threads, chained_with=['num_threads'])
ScenarioConfig = 'This ScenarioConfig var will be automatically linked to task configuration later in ./MISSION/env_router.py'
backup_files = [] # a list of files that needs to be backed up at each run
hmap_logger = None # this is just a global logger
heartbeat_on = True # some fancy commandline visual effect to show that envirenment is running
cfg_ready = False # DO NOT change! automatically set to True when Json configuration is all locked-and-loaded
# ! uploading "./RESULT/$note" to a data storage server
allow_res_upload = True # upload results to a data storage server when exiting
upload_after_test = False # upload results to a data storage server when completing a test run
machine_info = 'auto load, do not change this var!'
remote_server_ops = ""
# KEY = {"addr": None, "usr":None, "pwd":None} # data storage server ip addr, username and password