rl_gym_examples

This repository contains examples of common Reinforcement Learning algorithms in openai gymnasium environment, using Python.

This repo records my implementation of RL algorithms while learning, and I hope it can help others learn and understand RL algorithms better.

✨Features

Document for each algorithm: Every folder has a README.md file to introduce the algorithm
Examples in OpenAI Gymnasium environments
Detailed comments

Choose the version you want to use:

[Simple Implementation]: The simplest implementation of each algorithm, showing the core logic of the algorithm.

First, install the dependencies, you can install dependencies using conda or pip:
- conda (recommended)
Create a new conda environment using the yml file:
```
conda create -f rl_gym_examples.yml
```
- pip
You can also install the dependencies using pip(though it is not recommended):
```
pip install -r requirements.txt
```
The Python version is 3.8.
Then, you can run the examples in the corresponding folders, for example:
```
cd dp
python gym_cliff_walking.py
```

The pytorch in the dependencies is cpu version, you can install the gpu version by following the instructions in the pytorch website.

Algorithm	Observation Space	Action Space	Model-based or Model-free	On-policy or Off-policy
Dynamic Programming(Policy Iteration or Value Iteration)	Discrete	Discrete	Model-based	NA
Sarsa	Discrete	Discrete	Model-free	on-policy
Q-learning	Discrete	Discrete	Model-free	off-policy
DQN	Continuous	Discrete	Model-free	off-policy
REINFORCE	Continuous	Discrete/Continuous	Model-free	on-policy
Actor-Critic	Continuous	Discrete/Continuous	Model-free	on-policy
TRPO/PPO	Continuous	Discrete/Continuous	Model-free	on-policy
DDPG	Continuous	Continuous	Model-free	off-policy
SAC	Continuous	Continuous	Model-free	off-policy