Deep RL algorithms implemented using Pytorch

Algo list:

1. DQN
2. Vanilla policy Gradient
3. Deep Deterministic Policy Gradient
4. Twin Delayed Deep Deterministic Policy Gradient
5. Soft Actor Critic
6. Proximal Policy Optimization - CLIP

Article on deeper Look into policy gradients

Experimental Results:

Algorithm	Discrete Env: LunarLander-v2	Continuous Env: Pendulum-v0
DQN		-
VPG		-
DDPG	-
TD3	-
SAC	-
PPO	-

Resources:

1. RL course by David Silver
2. Lecture slides for above course
3. Spinning up by OpenAI
4. More exhaustive RL guide by Deeny Britz

Installation:

Prerequisites:

• Python 3.8+ (3.8.10 recommended)
• pyenv (optional but recommended)
• Homebrew (for macOS users)

Setup:

1. Clone the repository:

   git clone https://github.com/akashe/DeepReinforcementLearning.git
   cd DeepReinforcementLearning

2. Set up Python environment:

   # Option A: Using pyenv (recommended)
   pyenv install 3.8.10
   pyenv local 3.8.10
   
   # Option B: Using system Python 3.8+
   # Make sure you have Python 3.8+ installed

3. Install system dependencies (for Box2D environments like LunarLander):

   # macOS:
   brew install swig
   
   # Ubuntu/Debian:
   sudo apt-get install swig
   
   # Other systems: install swig through your package manager

4. Create and activate virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

5. Install Python dependencies:

   pip install -r requirements.txt

Usage:

Just run the file/algorithm directly. There is no common structures between algorithms as I implemented them as I learnt them. Different algorithms are inspired from different sources.

Examples:

python ddpg.py          # Run DDPG on Pendulum-v1
python DQN.py           # Run DQN on LunarLander-v2
python td3.py           # Run TD3 on Pendulum-v1
python SoftActorCritic.py # Run SAC on Pendulum-v1