rld 0.1.1

Description:

rld 0.1.1

A development tool for evaluation and interpretability of reinforcement learning agents.

Installation
pip install rld

Usage
Firstly, calculate attributations for your rollout using:
rld attribute [--rllib] [--out <ROLLOUT>] config.py <INPUT_ROLLOUT>

This will take INPUT_ROLLOUT (possibly in the Ray RLlib format, if --rllib is set)
and calculate attributations for each timestep in each trajectory,
using the configuration stored in config.py.
The output file will be stored as ROLLOUT.
See the Config class for possible configuration.
Once the attributations are calculated, you can visualize them using:
rld start --viewer <VIEWER_ID> <ROLLOUT>

See the examples for reference.
Description
rld provides a set of tools to evaluate and understand behaviors of reinforcement
learning agents. Under the hood, rld uses Captum to calculate
attributations of observation components. rld is also integrated with
Ray RLlib library and allows to load agents trained in RLlib.
Current limitations
rld is currently in its early development stage, thus the functionality is very limited.
RL algorithms
rld is algorithm-agnostic, but currently it is more suitable for policy-based methods.
This is due to the fact that the Model is now expected to output logits for a given
observation. This, however, will change in the future, and rld will support more
algorithms.
Viewers
This is the list of viewers, which ship with rld:

none
cartpole
atari

You can easily create your own viewer, for your own environment, but to make it visible
for rld, you have to rebuild the project. This will be improved in the future.
Observation and action spaces
The table below presents currently supported observation and action spaces.




Action space




Discrete
MultiDiscrete


Obs space
Box
:heavy_check_mark:
:heavy_check_mark:


Dict
:heavy_check_mark:
:heavy_check_mark:


Roadmap
See the issues page to see the list of
features planned for the future releases. If you have your own ideas,
you are encouraged to post them there.