🤖 REINFORCEpy

Implementation of the REINFORCEjs library from Kaparthy in Python. The original library has been implemented in JavaScript. The objective of this repository is to implement the RL algorithms and the demos in Python.

Note that this is not a 1-to-1 implementation in Python. The idea is simply trying to develop similar algorithms and demos as shown in Kaparthy's library.

Value Iteration

We started by implemented the most trivial algorithm, Value Iteration, from scratch.

The following shows an example of the value function for different iterations.

Value function after $1$ iteration	Value function after $100$ iteration

🏃 How to Run?

There are multiple parameters which can be chosen to set when running the main.py. An example call would look like this:

python main.py \
    --seed=42 \
    --verbose=1 \
    --episodes=1 \
    --timesteps=1 \
    --grid_size=10 \
    --algo=value_iteration \
    --render_large=True \
    --render_with_values=True

All supported arguments are listed below:

usage: 
  main.py [--seed] [--verbose] [--episodes] [--timesteps] [--grid_size] [--algo] 
          [--render_large] [--render_with_values]

Argument	Help	Default
--seed	random seed	$42$
--verbose	verbosity level	$1$
--episodes	number of episodes	$1$
--timesteps	maximal number of timesteps	$1,000$
--grid_size	size of the gridworld	$10$
--algo	learning algorithm	value_iteration
--render_large	render large gridworld	False
--render_with_values	render gridworld with value estimates	False

📝 ToDo's

Added to docs/changelog.md