# sourecode of XingTian
cd XingTian
# training task
xt_main -f examples/cartpole_ppo.yaml
# evaluate task
xt_main -f examples/cartpole_ppo.yaml -t evaluate
# show help
>>> xt_main --help
usage: xt_main [-h] -f CONFIG_FILE [-s3 SAVE_TO_S3]
[-t {train,evaluate,train_with_evaluate}] [-v VERBOSITY]
XingTian Usage.
optional arguments:
-h, --help show this help message and exit
-f CONFIG_FILE, --config_file CONFIG_FILE
config file with yaml
-s3 SAVE_TO_S3, --save_to_s3 SAVE_TO_S3
save model/records into s3 bucket.
-t {train,evaluate,train_with_evaluate}, --task {train,evaluate,train_with_evaluate}
task choice to run xingtian.
-v VERBOSITY, --verbosity VERBOSITY
logging.set_verbosityWhen the starting, the workspace path will be printed. The model and related evaluation results are saved in the workspace.
INFO Dec 26 11:08:22: **********
workspace:
/home/User/xt_archive/xt_CartPole-v0_PPO+201226110822T0The parameters of the XingTian's training task consist of four modules: agent, algorithm, model, and environment. Users can combine defined modules by setting the parameters of the four modules. For details about how to customize modules, please see the developer guide. The Benchmark parameter is used to configure and compare result data.
The parameters are described as follows:
alg_para: # parameter for algorithm module
alg_name: PPO # Algorithm name registed, class name as default
alg_config:
process_num: 1 # train with multiprocess(coming soon)
only_save_best_model: True # model save strategy (coming soon)
env_para: # parameter for Environment module
env_name: GymEnv # Environment name registed, class name as default
env_info: # game name
name: CartPole-v0
vision: False
agent_para: # Agent Parameter
agent_name: PPO # Agent name registed, class name as default
agent_num : 1 # agent number live under the same environment
agent_config:
max_steps: 200 # max interaction step within each episode
complete_step: 50000 # whole explore steps in once train task
model_para: # Model Parameter
actor: # By default, contains a model named actor.
model_name: PpoMlp # Model name registed, class name as default
state_dim: [4] # dimensions of state
action_dim: 2 # dimensions of action
summary: False # summary model to log
env_num: 10 # The number of explorer to start up in each node
# set the IP, account, and password of your machine,
# If you want to configure a distributed training network,
# just list all of IP, account, and password for each machine here.
# NOTE:
# 1. use a local node, User do not need to set 'node_config' or 'test_node_config'. The system automatically configures it.
# 2. use a non-local node, User must set all node information, including the local node account information.
# node_config: [["127.0.0.1", "username", "passwd"]] # account for each node
# test_node_config: [["127.0.0.1", "user", "passwd"]] # evaluate node account
# remote_env: # remote env
# conda: /home/test_user/anaconda2/envs/xt_qmix # remove conda env set
# env: # remote env set
# SC2PATH: /home/test_user/user-proj/marl_sim/StarCraftII
# no_proxy: "192.168.1.*,127.0.0.1,10.*"
# benchmark: # benchmark info
# The plus sign (+) is a connector in the ID. If the character contains the plus sign (+), the system directly uses the ID and does not add information such as the timestamp.
# id: xt_cartpole # default: default_ENV_ALG ('+'.join([ID, START_time]))
# archive_root: ./xt_archive # default: ~/xt_archive # Root directory for archiving evaluation information, which is automatically allocated.
# eval:
# model_path: /xt_archive/model_data/cartpole_0 # model file to evaluate
# gap: 20 # evaluate interval after training
# model_divided_freq: 1 # how many times to divided of each model
# episodes_per_eval: 2 # Number of episodes run for each evaluation
# evaluator_num: 1 # Setting the number of instances that support concurrent evaluation
# max_step_per_episode: 2000 # Maximum number of steps for each evaluation
By default, TensorboardX is used to display the training status information, and the task-related records are stored in the workspace directory.
The benchmark directory stores key information such as the parameter configuration and train/evaluate reward information of the training task.
/home/User/xt_archive/xt_CartPole-v0_PPO+201226110822T0
|-- benchmark
| |-- records.csv
| `-- train_config.yaml
|-- events.out.tfevents.1608952102.SZXXXXXXXXXX
|-- models
| |-- actor_00000.npz
| |-- actor_00100.npz
| |-- actor_00200.npz
| |-- actor_00300.npz
| |-- actor_00400.npz
| `-- actor_00500.npz
`-- train_records.json