wjwgym(暂名): 强化学习工具包
基于Pytorch与OpenAI Gym实现强化学习训练涉及到：
- 强化学习智能体编写
- 神经网络创建
- gym环境编写
wjwgym提供了常用强化学习智能体 DQN DDPG QLearning 的pytorch实现，完成了其中的通用部分。可以作为智能体编写的参考，也可以直接作为基类用于创建自己的强化学习智能体。同时实现了简单的神经网络和gym环境，可以作为实现的参考。
Agents
wjwgym.agents 中实现了DQNBase DDPGBase 和 LinearBase 三个基类，分别作为DQN DDPG 和 QLearning/Sarsa 的通用抽象。
- DQN_base
或称DoubleDQN，在标准DQN基础上增加了经验回放和fixed_target。实现了标准的训练获取动作添加记录到经验回放池等方法，同时提供了自己实现神经网络构建的接口。 - DDPG_base
实现了DDPG通用的训练获取动作添加记录到经验回放池方法，同时提供Actor和Critic神经网络创建的接口。有默认的噪声函数实现，也支持自己实现噪声函数。 - Linear_base 实现了线性学习通用的QTable，但训练函数需要自己创建。
Models
wjwgym.models 中实现了几种简单的神经网络，用于创建示例。
Envs
wjwgym.envs 中实现了简单的环境用于创建示例，在wjwgym.envs._init__中给出了将环境注册到gym中的方法。需要注意：
- 需要将包安装到site-packages下
- 需要
import wjwgym或者from wjwgym import envs，__init__文件中的注册代码才会生效。
1 maintainer
Metadata
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Release files for wjwgym 0.1.2
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