This repository is the source code of paper SEM: Adaptive Staged Experience Access Mechanism for Reinforcement Learning of ICTAI 2020.
"""
Usage:
python [options]
Options:
-h,--help Help
-i,--inference Inference mode [default: False]
-a,--algorithm=<name> Specify training algorithm [default: ppo]
-c,--config-file=<file> Specify the hyper-parameter configuration file for the model [default: None]
-e,--env=<file> Specify the unity environment name [default: None]
-p,--port=<n> Specify port [default: 5005]
-u,--unity Whether to use the unity client [default: False]
-g,--graphic Whether to display graphical interface [default: False]
-n,--name=<name> Specify the name of this training [default: None]
-s,--save-frequency=<n> Specify the frequency for saving model [default: None]
-m,--models=<n> How many models to train at the same time [default: 1]
--store-dir=<file> Specify the folder path to save the model, log, and data [default: None]
--seed=<n> Specify the random seed of the model [default: 0]
--max-step=<n> Maximum time step per episode [default: None]
--max-episode=<n> Total training episodes [default: None]
--sampler=<file> Specify the file path for the random sampler for Unity [default: None]
--load=<name> Specify the name of the training to load the model [default: None]
--fill-in Specify whether to pre-populate the experience pool to batch_size [default: False]
--prefill-choose Specify whether to choose action while pre-populate the experience pool [default: False]
--gym Whether to use a gym training environment [default: False]
--gym-agents=<n> Specify the amount of parallel training [default: 1]
--gym-env=<name> Specify the name of the gym environment [default: CartPole-v0]
--gym-env-seed=<n> Specify random seed for gym environment [default: 0]
--render-episode=<n> Specify when the gym environment starts rendering [default: None]
--info=<str> Write a description of the training, wrapped in double quotation marks [default: None]
--sem Whether to use SEM or not [default: False]
Example:
python run.py --gym --gym-env Hopper-v2 -a td3 -n test --seed 0
"""
Train with SEM:
python run.py --gym --gym-env [env_id] -a [algo_name] -n [training_name] --sem
Inference Policies:
python run.py --gym --gym-env [env_id] -a [algo_name] -n [training_name] -i