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, StarCraftII 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.
# ubuntu 18.04
sudo apt-get install python3-pip libopencv-dev -y
pip3 install opencv-python
# run with tensorflow 1.15.0 or tensorflow 2.3.1
pip3 install zmq h5py gym[atari] tqdm imageio matplotlib==3.0.3 Ipython pyyaml tensorflow==1.15.0 pyarrow lz4 fabric2 absl-py psutil tensorboardX setproctitle
or, using pip3 install -r requirements.txt
If your want to used PyTorch as the backend, please install it by yourself. Ref Pytorch
# 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]: '0.3.0'
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
alg_config:
process_num: 1
save_model: True # default False
save_interval: 100
env_para:
env_name: GymEnv
env_info:
name: CartPole-v0
vision: False
agent_para:
agent_name: PPO
agent_num : 1
agent_config:
max_steps: 200
complete_step: 1000000
complete_episode: 3550
model_para:
actor:
model_name: PpoMlp
state_dim: [4]
action_dim: 2
input_dtype: float32
model_config:
BATCH_SIZE: 200
CRITIC_LOSS_COEF: 1.0
ENTROPY_LOSS: 0.01
LR: 0.0003
LOSS_CLIPPING: 0.2
MAX_GRAD_NORM: 5.0
NUM_SGD_ITER: 8
SUMMARY: False
VF_SHARE_LAYERS: False
activation: tanh
hidden_sizes: [64, 64]
env_num: 10
In addition, your could find more configuration sets in examples directory.
python3 xt/main.py -f examples/cartpole_ppo.yaml -t train
Set benchmark.eval.model_path
for evaluation within the YOUR_CONFIG_FILE.yaml
benchmark:
eval:
model_path: /YOUR/PATH/TO/EVAL/models
gap: 10 # index gap of eval model
evaluator_num: 1 # the number of evaluator instance
# run command
python3 xt/main.py -f examples/cartpole_ppo.yaml -t evaluate
NOTE: XingTian start with
-t train
as default.
# Could replace `python3 xt/main.py` with `xt_main` command!
xt_main -f examples/cartpole_ppo.yaml -t train
# train with evaluate
xt_main -f examples/cartpole_ppo.yaml -t train_with_evaluate
your_train_configure.yaml
xt_main -f path/to/your_train_configure.yaml
:)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 | 4877 | 2807 | ~1800 |
Breakout | 341 | 104 | ~250 |
QBert | 14771 | 11085 | ~14000 |
SpaceInvaders | 1025 | 671 | ~800 |
IMPALA Reward after 10M time-steps (40M frames).
env | XingTian IMPALA | RLlib IMPALA |
---|---|---|
BeamRider | 2313 | 2071 |
Breakout | 334 | 385 |
QBert | 12205 | 4068 |
SpaceInvaders | 742 | 719 |
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 | 2422 | 1618 |
Breakout | 2497 | 1535 |
QBert | 2436 | 1617 |
SpaceInvaders | 2438 | 1608 |
IMPALA
env | XingTian IMPALA | RLlib IMPALA |
---|---|---|
BeamRider | 8756 | 3637 |
Breakout | 8814 | 3525 |
QBert | 8249 | 3471 |
SpaceInvaders | 8463 | 3555 |
Experiment condition: 72 Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz with single Tesla V100
Ray's reward data come from https://github.com/ray-project/rl-experiments, and Throughout from ray 0.8.6 with the same machine condition.
XingTian refers to the following projects: DeepMind/scalable_agent, baselines, ray.
The MIT License(MIT)