Deep Q learning agent playing 2048
- python 3.6
It's recommended to use conda
conda create --name tf-cpu tensorflow python=3.6
or gpu version:
conda create --name tf-gpu tensorflow-gpu
conda install --file requirements.txt
pip3 install recordclass
You may also use pure pip:
pip3 install -r requirements.txt
pip3 install recordclass
You may have to manually install python3-tk for tkinter library on linux
Use training_cli.py script to:
- train random or specific agent configuration
- render the game while training
- find agent with maximum average score
- watch best agent play
python3 training_cli.py --help
Implementation of deep q learning agent for playing 2048 game
Each agent is evaluated by playing 25000 steps of the game
Best agent goes up to 2804 sum of tiles and max 2048 tile with average sum on board ~1100
Max tile of random agent is 256 with average sum on board equal 250
ill0_em015_Adamax_mae_256_256_256_linear_batch_8_tUpdF100_learnF30_div_by_max_ddq_1_trm_epsC10000_RMDC1000000_dry1
ill0 - don't allow illegal moves
em015 - minimum eps = 0.15
Adamax - neural network optimizer
256_256_256 - sizes of each fully connected deep layer
linear - output activation function
batch_8 - number of memory samples in each training
tUpdF100 - update target network (double q learning) each 100 trainings
learnF30 - train each 30 steps in game
div_by_max - state mapping function, this one divides all tiles by current max
ddq_1 - use double q learning
trm - type of rewarding function, time reward minus (see helper_functions.py for details)
epsC10000 - epsilon constant (used in )
RMDC1000000 - type of replay memory, Replay Memory Dynamic Crucial with 1e6 max capacity
dry1 - dry training mode, in each state test and remember outcomes from all moves