Trains a Pong agent using policy gradients on OpenAI's gym. This code was copied from Andrej Karpathy's Deep Reinforcement Learning: Pong from Pixels, and almost all changes to the code were for cosmetic purposes. Please refere to Karpathy's walkthrough to learn more about the implementation!
python3 pong.py
Set resume = True in pong.py if you want to continue training the agent where it was left off in model.p, otherwise set resume = False to start the agent training from scratch.
Resuming model 'model.p'...
ep 0: game finished, reward: 1.000000
ep 0: game finished, reward: 1.000000
ep 0: game finished, reward: 1.000000
ep 0: game finished, reward: -1.000000
ep 0: game finished, reward: 1.000000
ep 0: game finished, reward: -1.000000
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