A Pong AI trained using policy gradients, implemented using TensorFlow and OpenAI gym, based on Andrej Karpathy's Deep Reinforcement Learning: Pong from Pixels.

After 7,000 episodes of training, the result looks like:

First, install OpenAI Gym and TensorFlow.

Run without any arguments to train the AI from scratch. Checkpoints will be saved every so often (see --checkpoint_every_n_episodes).

Run with --load_checkpoint --render to see how an AI trained on ~8,000 episode plays.

• 'Round': one match, in which one player gains a point
• 'Episode': a set of rounds that make up one game (usually around 20 or so - I'm not sure what logic the game uses to decide this)

• It takes about 500 episodes to see whether the agent is improving or not
• It takes about 7,000 episodes to get to a stage where the agent is winning half and losing half of the rounds

• Andrej calculates gradients for each episode, accumulates them over a batch size of 10 episodes, and then applies them all in one go. I think this is based on a recommendation in Asynchronous Methods for Deep Reinforcement Learning. It looked like this was going to be a pain to do in TensorFlow, though, (see e.g. http://stackoverflow.com/q/37710974), so here we just use a batch size of one episode.
• Andrej uses RMSProp, but here we use Adam. (RMSProp wouldn't work - the AI would never improved - and I was never able to figure out why.)

When you have a hypothesis that you want to test, think deliberately about what the cheapest way to test it is.

For example, for a while things weren't working, and while debugging I noticed that Andrej's code initialises his RMSProp gradient history with zeros, while TensorFlow initialises with ones. I hypothesised that this was a key factor, and the test I came up with was to compile a custom version of TensorFlow with RMSProp initialised using zeros. It later occurred to me that a much cheaper test would have been to just change Andrej's code to initialise with ones instead.

Acknowledging explicitly to yourself when you've got a hypothesis you want to test rather than just randomly testing stuff out in a state of flow may help with this.