CZ3004 MDP algorithm implemented using reinforcement learning
- change the path to grid in
gridworld.py
get_grid_from_file(<path>)
to the path of your grid - change parameters
_episodes
and_training_episodes
ingridworld.py
to desired value - (optional setup)
- parameter
_epsilon
ingridworld.py
to your desired exploration rate - parameter
__speed
ingridworld.py
to your desired number of movement per second - parameter
pauseCallback
ingridworld.py
- function callback
graphic_display
ingridworld.py
line366 tonull_display
- parameter
- after training, remember to save weights manually in
__init__()
inqlearningAgents.py
You can design your own features by modifying get_features()
in state.py
and change the
reward function in getReward()
in gridworld.py
respectively