Implementation of the proximal policy optimization on Atari environments. All hyper-parameters have been chosen based on the paper.

For continuous domain (Mujoco) look at this.

• gym == 0.17.2
• numpy == 1.19.1
• opencv_contrib_python == 3.4.0.12
• torch == 1.4.0
• tqdm == 4.47.0

pip3 install -r requirements.txt

The training requires a suitable (not necessarily a 1080 Ti or a 2080 RTX Nvidia gpu 😁) gpu-enabled machine. Google Colab provides what is enough to train such an algorithm but if you a more powerful free online gpu provider, take a look at: paperspace.com.

• To run the code:

python3 main.py

• If you want continue the previous training procedure, turn LOAD_FROM_CKP to True otherwise, the training would be restarted from scratch.
• If you want to test the agent, simply turn Train flag to False. There is a pre-trained model in the Pre-trained models directory that you may use to see agent plays.

• Pong
• Breakout
• MsPacman

• Following graphs are breakout environment's result.

Proximal Policy Optimization Algorithms, Schulman et al., 2017

@OpenAI for Baselines.
@higgsfield for his ppo code.