Playing Atari's Pong with Reinforcement Learning

Deep Q Learning (DQN)

Hardware: Google Colab T4

Model Type	Average Reward	Training Time	Total Training Steps
PPO	21.0	5:32:21	10,000,000
DQN	20.6	11:56:00	10,000,000

When training with Google Colab Notebooks with high memory option enabled, try not to exceed the buffer size 850,000 as you can run into memory issues
When training in more complex environments or using multiple simulated environments (n_evn > 1), DQN is very sensitive to the hyperparameter settings
Stable Baselines3 implementation of Soft Actor-Critic (SAC) only supports continuous action spaces and can not be used with Atari's Pong as it uses discrete actions
When using rllib, be mindful of your resources, as the training jobs might not start (always in pending status) if there are not enough CPUs or GPUs allocated

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[Atari Pong] Multi-Agent Reinforcement Learning PPO.ipynb		[Atari Pong] Multi-Agent Reinforcement Learning PPO.ipynb
[Atari Pong] Single-Agent Reinforcement Learning DQN.ipynb		[Atari Pong] Single-Agent Reinforcement Learning DQN.ipynb
[Atari Pong] Single-Agent Reinforcement Learning PPO.ipynb		[Atari Pong] Single-Agent Reinforcement Learning PPO.ipynb