PARL is a flexible, distributed and object-oriented programming reinforcement learning framework.
Features¶
Object Oriented Programming |
Distributed Training |
class MLPModel(parl.Model):
def __init__(self, act_dim):
self.fc1 = layers.fc(size=10)
self.fc2 = layers.fc(size=act_dim)
def forward(self, obs):
out = self.fc1(obs)
out = self.fc2(out)
return out
model = MLPModel()
target_model = copy.deepcopy(model)
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# Absolute multi-thread programming
# witout the GIL limitation
@parl.remote_class
class HelloWorld(object):
def sum(self, a, b):
return a + b
parl.connect('localhost:8003')
obj = HelloWorld()
ans = obj.sum(a, b)
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Abstractions¶
PARL aims to build an agent for training algorithms to perform complex tasks.
The main abstractions introduced by PARL that are used to build an agent recursively are the following:
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 the 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.
Installation
Features
Parallel Training
High-quality Implementations