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README.md

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 ## Introduction
 
-This repository provides a Minimal PyTorch implementation of Proximal Policy Optimization (PPO) with clipped objective for OpenAI gym environments. It is primarily intended for beginners in [Reinforcement Learning](https://en.wikipedia.org/wiki/Reinforcement_learning) for understanding the PPO algorithm. It can still be used for complex environments but may require some hyperparameter-tuning or changes in the code.
+This repository provides a Minimal PyTorch implementation of Proximal Policy Optimization (PPO) with clipped objective for OpenAI gym environments. It is primarily intended for beginners in [Reinforcement Learning](https://en.wikipedia.org/wiki/Reinforcement_learning) for understanding the PPO algorithm. It can still be used for complex environments but may require some hyperparameter-tuning or changes in the code. A concise explaination of PPO algorithm can be found [here](https://stackoverflow.com/questions/46422845/what-is-the-way-to-understand-proximal-policy-optimization-algorithm-in-rl) and a thorough explaination of all the details for implementing best performing PPO can be found [here](https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/) (All are not implemented in this repo yet). 
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 To keep the training procedure simple : 
  - It has a **constant standard deviation** for the output action distribution (**multivariate normal with diagonal covariance matrix**) for the continuous environments, i.e. it is a hyperparameter and NOT a trainable parameter. However, it is **linearly decayed**. (action_std significantly affects performance)
  - It uses simple **monte-carlo estimate** for calculating advantages and NOT Generalized Advantage Estimate (check out the OpenAI spinning up implementation for that).
  - It is a **single threaded implementation**, i.e. only one worker collects experience. [One of the older forks](https://github.com/rhklite/Parallel-PPO-PyTorch) of this repository has been modified to have Parallel workers
 
-A concise explaination of PPO algorithm can be found [here](https://stackoverflow.com/questions/46422845/what-is-the-way-to-understand-proximal-policy-optimization-algorithm-in-rl)
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 ## Usage
 
 - To train a new network : run `train.py`