Simple code to demonstrate Deep Reinforcement Learning by using Proximal Policy Optimization and Random Network Distillation in Tensorflow 2 and Pytorch
Version 2 will bring improvement in code quality and peformance. I refactor the code so it will follow algorithm in PPO's implementation on OpenAI's baseline. I also using 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).
- Use Pytorch and Tensorflow 2
- Clean up the code
- Use Truly PPO
- Add more complex environment
- Add more explanation
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
Make sure you have installed Pytorch and Gym.
- Click here to install gym
You can use either Pytorch or Tensorflow 2
Just clone this project into your work folder
git clone https://github.com/wisnunugroho21/reinforcement_learning_ppo_rnd.git
After you clone the project, run following script in cmd/terminal :
cd reinforcement_learning_ppo_rnd/PPO_RND/pytorch
python ppo_rnd_frozen_notslippery_pytorch.py
cd reinforcement_learning_ppo_rnd/PPO_RND/'tensorflow 2'
python ppo_frozenlake_notslippery_tensorflow.py
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.
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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.
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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
Random Network Distillation (RND), a prediction-based method for encouraging reinforcement learning agents to explore their environments through curiosity, which for the first time exceeds average human performance on Montezuma’s Revenge. RND achieves state-of-the-art performance, periodically finds all 24 rooms and solves the first level without using demonstrations or having access to the underlying state of the game.
RND incentivizes visiting unfamiliar states by measuring how hard it is to predict the output of a fixed random neural network on visited states. In unfamiliar states it’s hard to guess the output, and hence the reward is high. It can be applied to any reinforcement learning algorithm, is simple to implement and efficient to scale.
You can read full detail of RND in here
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
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This project is far from finish and will be improved anytime . Any fix, contribute, or idea would be very appreciated