This repository contains a set of popular Deep Reinforcement Learning algorithms tested on OpenAI's Gym. The objective was to have simple and clear implementations.

Each folder corresponds to a particular algorithm which contains the required theoretical material to get started.

To install the packages locally, the following steps are suggested.

conda create -n RL python=3
source activate RL
git clone https://github.com/Suman7495/Deep-Reinforcement-Learning.git
cd Deep-Reinforcement-Learning

To run any of the algorithms, go to the specific directory and run main.py. For example:

cd COPOS
python main.py

Additionally, you can enter optional commands. For example:

python main.py --env 'CartPole-v0'

Here are some key resources to get started with Reinforcement Learning.

1. Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto.
2. Reinforcement Learning: State of the Art

1. RL Course by David Silver
2. Deep Reinforcement Learning UC Berkeley