Deep Q-Learning Project to play 2048. See this presentation for an introduction.

Install TensorFlow, python & pip. Then, run:

pip install -r requirements.txt

To run the code, you'll need to update your PYTHONPATH:

source set_pythonpath.sh

Now, you should be able to run the tests:

py.test

All python source code lives in py_2048_rl.

This directory contains code to simulate the 2048 game itself. For example, it provides a Game class that implements the game logic. The play module defines the Experience class, a play() function and various strategies that can be passed as an argument to play().

This directory contains all code that has to do with the Deep Q-Learning algorithm itself. Here's a comprehensive list of the modules:

• replay_memory implements the Replay Memory. Main methods are add() to add an experience and sample() to sample a number of experiences.
• experience_collector implements a collect(strategy, num_games) function that plays a number of games, deduplicates & undersamples the experiences, and returns them.
• target_batch_computer is responsible for computing the target batch that is passed to the network.
• experience_batcher uses the ReplayMemory, ExperienceCollector and TargetBatchComputer to generate training batches for the neural network.
• model defines the Neural Network architecture and its training parameters (e.g. Learning Rate).
• learning glues everything together to implement the Deep Q-Learning algorithm.

Step 1 is to set various parameters.

For example, you might want to adjust

• The GAMMA value in target_value_computer.py
• The INIT_LEARNING_RATE or HIDDEN_SIZES in model.py
• The MIN_EPSILON in experience_batcher.py
• ...

Once that's done, you can simple run python py_2048_rl/learning/learning.py <train_dir>.

You can use TensorBoard to monitor your Network training, simply by passing you train directory as the --logdir param. Furthermore, have a look at py_2048_rl/analisis.py (for plotting a historgram of Q-Values) and py_2048_rl/play_game.py (for simulating a (number of) games given a particular model).