reinforcement_learning_truly_ppo

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reinforcement_learning_truly_ppo

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Deep Reinforcement Learning by using Truly Proximal Policy Optimization in Tensorflow 2 and Pytorch

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# Truly PPO

Simple code to demonstrate Deep Reinforcement Learning by using Truly Proximal Policy Optimization in Tensorflow 2 and Pytorch.

The code is use newer version of PPO called Truly PPO, which has more sample efficiency and performance than OpenAI's PPO. Currently, I am focused on how to implement this project in more difficult environment (Atari games, MuJoCo, etc).

- [x] Use Pytorch and Tensorflow 2
- [x] Clean up the code
- [x] Use Truly PPO
- [ ] Add more complex environment
- [ ] Add more explanation

## Getting Started

This project is using Pytorch and Tensorflow 2 for Deep Learning Framework and using Gym for Reinforcement Learning Environment.  
Although it's not required, but i recommend run this project on a PC with GPU and 8 GB Ram

### Prerequisites

Make sure you have installed Pytorch and Gym.  
- Click [here](https://gym.openai.com/docs/) to install gym

You can use either Pytorch or Tensorflow 2
- Click [here](https://pytorch.org/get-started/locally/) to install pytorch
- Click [here](https://www.tensorflow.org/install) to install tensorflow 2

### Installing

Just clone this project into your work folder

```
git clone https://github.com/wisnunugroho21/reinforcement_learning_truly_ppo.git
```

## Running the project

After you clone the project, run following script in cmd/terminal :

#### Pytorch version
```
cd reinforcement_learning_truly_ppo/PPO/pytorch
python ppo_pytorch.py
```

#### Tensorflow 2 version
```
cd reinforcement_learning_truly_ppo/PPO/'tensorflow 2'
python ppo_tensorflow.py
```

## Proximal Policy Optimization

PPO is motivated by the same question as TRPO: how can we take the biggest possible improvement step on a policy using the data we currently have, without stepping so far that we accidentally cause performance collapse? Where TRPO tries to solve this problem with a complex second-order method, PPO is a family of first-order methods that use a few other tricks to keep new policies close to old. PPO methods are significantly simpler to implement, and empirically seem to perform at least as well as TRPO.

There are two primary variants of PPO: PPO-Penalty and PPO-Clip.

* PPO-Penalty approximately solves a KL-constrained update like TRPO, but penalizes the KL-divergence in the objective function instead of making it a hard constraint, and automatically adjusts the penalty coefficient over the course of training so that it’s scaled appropriately.

* PPO-Clip doesn’t have a KL-divergence term in the objective and doesn’t have a constraint at all. Instead relies on specialized clipping in the objective function to remove incentives for the new policy to get far from the old policy.

OpenAI use PPO-Clip  
You can read full detail of PPO in [here](https://spinningup.openai.com/en/latest/algorithms/ppo.html)

## Truly Proximal Policy Optimization

Proximal policy optimization (PPO) is one of the most successful deep reinforcement-learning methods, achieving state-of-the-art performance across a wide range of challenging tasks. However, its optimization behavior is still far from being fully understood. In this paper, we show that PPO could neither strictly restrict the likelihood ratio as it attempts to do nor enforce a well-defined trust region constraint, which means that it may still suffer from the risk of performance instability. To address this issue, we present an enhanced PPO method, named Truly PPO. Two critical improvements are made in our method: 1) it adopts a new clipping function to support a rollback behavior to restrict the difference between the new policy and the old one; 2) the triggering condition for clipping is replaced with a trust region-based one, such that optimizing the resulted surrogate objective function provides guaranteed monotonic improvement of the ultimate policy performance. It seems, by adhering more truly to making the algorithm proximal - confining the policy within the trust region, the new algorithm improves the original PPO on both sample efficiency and performance.

You can read full detail of Truly PPO in [here](https://arxiv.org/abs/1903.07940)

## Result

### LunarLander using PPO

| Result Gif  | Award Progress Graph |
| ------------- | ------------- |
| ![Result Gif](https://github.com/wisnunugroho21/reinforcement_learning_ppo_rnd/blob/master/Result/lunarlander.gif)  | ![Award Progress Graph](https://github.com/wisnunugroho21/reinforcement_learning_ppo_rnd/blob/master/Result/lunarlander_ppo.png)  |

### Bipedal using PPO

| Result Gif    |
| ------------- |
| ![Result Gif](https://github.com/wisnunugroho21/reinforcement_learning_ppo_rnd/blob/master/Result/bipedal.gif) |

### Pendulum using PPO

| Result Gif  | Award Progress Graph |
| ------------- | ------------- |
| ![Result Gif](https://github.com/wisnunugroho21/reinforcement_learning_ppo_rnd/blob/master/Result/pendulum.gif)  | ![Award Progress Graph](https://github.com/wisnunugroho21/reinforcement_learning_ppo_rnd/blob/master/Result/ppo_pendulum_tf2.png)  |

### Pong using PPO

| Result Gif    |
| ------------- |
| ![Result Gif](https://github.com/wisnunugroho21/reinforcement_learning_ppo_rnd/blob/master/Result/pong.gif) |

## Contributing
This project is far from finish and will be improved anytime . Any fix, contribute, or idea would be very appreciated

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