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PARL is a flexible and high-efficient reinforcement learning framework.
Reproducible. We provide algorithms that stably reproduce the result of many influential reinforcement learning algorithms.
Large Scale. Ability to support high-performance parallelization of training with thousands of CPUs and multi-GPUs.
Reusable. Algorithms provided in the repository could be directly adapted to a new task by defining a forward network and training mechanism will be built automatically.
Extensible. Build new algorithms quickly by inheriting the abstract class in the framework.
Model is abstracted to construct the forward network which defines a policy network or critic network given state as input.
Algorithm describes the mechanism to update parameters in Model and often contains at least one model.
Agent, a data bridge between the environment and the algorithm, is responsible for data I/O with the outside environment and describes data preprocessing before feeding data into the training process.
Note: For more information about base classes, please visit our tutorial and API documentation.
PARL provides a compact API for distributed training, allowing users to transfer the code into a parallelized version by simply adding a decorator. For more information about our APIs for parallel training, please visit our documentation.
Here is a Hello World example to demonstrate how easy it is to leverage outer computation resources.
#============Agent.py=================
@parl.remote_class
class Agent(object):
def say_hello(self):
print("Hello World!")
def sum(self, a, b):
return a+b
parl.connect('localhost:8037')
agent = Agent()
agent.say_hello()
ans = agent.sum(1,5) # it runs remotely, without consuming any local computation resourcesTwo steps to use outer computation resources:
- use the
parl.remote_classto decorate a class at first, after which it is transferred to be a new class that can run in other CPUs or machines. - call
parl.connectto initialize parallel communication before creating an object. Calling any function of the objects does not consume local computation resources since they are executed elsewhere.
For users, they can write code in a simple way, just like writing multi-thread code, but with actors consuming remote resources. We have also provided examples of parallized algorithms like IMPALA, A2C. For more details in usage please refer to these examples.
- Python 3.6+(Python 3.8+ is preferable for distributed training).
- paddlepaddle>=2.3.1 (Optional, if you only want to use APIs related to parallelization alone)
pip install parl
Several-points to get you started:
- Tutorial : How to solve cartpole problem.
- Xparl Usage : How to set up a cluster with
xparland compute in parallel. - Advanced Tutorial : Create customized algorithms.
- API documentation
For beginners who know little about reinforcement learning, we also provide an introductory course: ( Video | Code )
- QuickStart
- DQN
- ES
- DDPG
- A2C
- TD3
- SAC
- QMIX
- MADDPG
- PPO
- CQL
- IMPALA
- Winning Solution for NIPS2018: AI for Prosthetics Challenge
- Winning Solution for NIPS2019: Learn to Move Challenge
- Winning Solution for NIPS2020: Learning to Run a Power Network Challenge
