xingtian: A Python repository from tickpeach

Introduction

XingTian (刑天) is a componentized library for the development and verification of reinforcement learning algorithms. It supports multiple algorithms, including DQN, DDPG, PPO, and IMPALA etc, which could training agents in multiple environments, such as Gym, Atari, Torcs, StarCraft and so on. To meet users' requirements for quick verification and solving RL problems, four modules are abstracted: Algorithm, Model, Agent, and Environment. They work in a similar way as the combination of `Lego' building blocks. For details about the architecture, please see the Architecture introduction. Feel free to make good use of issue submission, or join our QQ chatroom (Chinese): 833345709.

Dependencies

# ubuntu 18.04
sudo apt-get install python3-pip libopencv-dev redis-server -y
pip3 install opencv-python

# run with tensorflow 1.15.0
pip3 install zmq h5py gym[atari] tqdm imageio matplotlib==3.0.3 Ipython pyyaml tensorflow==1.15.0 pyarrow lz4 fabric2 line_profiler redis absl-py psutil

or, using pip3 install -r requirements.txt

If your want to used PyTorch as the backend, please install it by yourself. Ref Pytorch

Installation

# cd PATH/TO/XingTian 
pip3 install -e .

After installation, you could use import xt; print(xt.__Version__) to check whether the installation is successful.

In [1]: import xt

In [2]: xt.__version__
Out[2]: '0.1.1'

Quick Start

Setup configuration

Follow's configuration shows a minimal example with Cartpole environment. More detailed description with the parameters of agent, algorithm and environment could been find in the User guide .

alg_para:
  alg_name: PPO
env_para:
  env_name: GymEnv
  env_info: {'name': CartPole-v0, 'vision': False}
agent_para:
  agent_name: CartpolePpo
  agent_num : 1
  agent_config: {
    'max_steps': 200,
    'complete_step': 500000}
model_para:
  actor:
    model_name:  PpoMlp
    state_dim: [4]
    action_dim: 2
    summary: True

env_num: 10

In addition, your could find more configuration sets in examples directory.

Start training task

python3 xt/main.py -f examples/cartpole_ppo.yaml -t train

Evaluate trained model

Set test_node_config and test_model_path for evaluation within the YOUR_CONFIG_FILE.yaml

python3 xt/main.py -f examples/cartpole_ppo.yaml -t evaluate

NOTE: XingTian start with -t train as default.

Run with CLI

# Could replace `python3 xt/main.py` with `xt_main` command!
xt_main -f examples/cartpole_ppo.yaml -t train

Develop with Custom case

Write custom module, and register it. More detail guidance on custom module can be found in the Developer Guide
Add YOUR-CUSTOM-MODULE name into your_train_configure.yaml
Start training with xt_main -f path/to/your_train_configure.yaml :)

Reference Results

Episode Reward Average

DQN Reward after 10M time-steps (40M frames).

env	XingTian Basic DQN	RLlib Basic DQN	Hessel et al. DQN
BeamRider	6706	2869	~2000
Breakout	352	287	~150
QBert	14087	3921	~4000
SpaceInvaders	947	650	~500

PPO Reward after 10M time-steps (40M frames).

env	XingTian PPO	RLlib PPO	Baselines PPO
BeamRider	4204	2807	~1800
Breakout	243	104	~250
QBert	12288	11085	~14000
SpaceInvaders	1135	671	~800

Throughput

DQN

env	XingTian Basic DQN	RLlib Basic DQN
BeamRider	129	109
Breakout	117	113
QBert	111	90
SpaceInvaders	115	100

PPO

env	XingTian PPO	RLlib PPO
BeamRider	1775	1618
Breakout	1801	1535
QBert	1741	1617
SpaceInvaders	1858	1608

Experiment condition： 72 Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz with single Tesla V100

Acknowledgement

XingTian refers to the following projects: DeepMind/scalable_agent, baselines, ray.

License

The MIT License(MIT)

tickpeach/xingtian