Metadata-Version: 2.1
Name: rlprop
Version: 0.0.4
Summary: Reinforcement Learning agents implemented in pytorch
Home-page: https://github.com/abstractpaper/prop
Author: Aziz Alfoudari
Author-email: aziz.alfoudari@gmail.com
License: UNKNOWN
Description: [![<abstractpaper>](https://circleci.com/gh/abstractpaper/prop.svg?style=shield)](https://circleci.com/gh/abstractpaper/prop)
        
        # prop
        
        prop is a library of Reinforcment Learning agents implemented in pytorch.
        
        ## Algorithms
        
        |         | Model      | Policy     |
        |---------|------------|------------|
        | **DQN** | Model-Free | Off-Policy |
        | **A2C** | Model-Free | On-Policy  |
        
        ## DQN
        
        [Deep Q-Learning][1] is a variant of Q-learning with a deep neural network used for estimating Q-values (hence DQN; Deep Q-Network).
        
        Both DQN and DDQN (Double DQN) are implemented.
        
        ## A2C
        
        [Advantage Actor Critic][2] is a variant of Actor-Critic that:
        - Uses a neural network to approximate a policy and a value function.
        - Computes the advantage of an action to scale the computed gradients. This acts as a vote of confidence (or skepticism) on actions produced by the actor.
        
        [1]: https://en.wikipedia.org/wiki/Q-learning#Deep_Q-learning
        [2]: https://hackernoon.com/intuitive-rl-intro-to-advantage-actor-critic-a2c-4ff545978752
        [3]: https://github.com/astooke/rlpyt
        [4]: https://github.com/openai/baselines
        [5]: https://github.com/NervanaSystems/coach
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
