Deep Reinforcement Learning in Pac-man
Run a model on smallGrid layout for 6000 episodes, of which 5000 episodes
are used for training.
$ python3 pacman.py -p PacmanDQN -n 6000 -x 5000 -l smallGrid
Different layouts can be found and created in the layouts directory
Parameters can be found in the params dictionary in pacmanDQN_Agents.py.
Models are saved as "checkpoint" files in the /saves directory.
Load and save filenames can be set using the load_file and save_file parameters.
Episodes before training starts: train_start
Size of replay memory batch size: batch_size
Amount of experience tuples in replay memory: mem_size
Discount rate (gamma value): discount
Learning rate: lr
Exploration/Exploitation (ε-greedy):
Epsilon start value: eps
Epsilon final value: eps_final
Number of steps between start and final epsilon value (linear): eps_step
Please cite this repository if it was useful for your research:
@article{van2016deep,
title={Deep Reinforcement Learning in Pac-man},
subtitle={Bachelor Thesis},
author={van der Ouderaa, Tycho},
year={2016},
school={University of Amsterdam},
type={Bachelor Thesis},
}
python==3.5.1tensorflow==0.8rc
DQN Framework by (made for ATARI / Arcade Learning Environment)
Pac-man implementation by UC Berkeley:
