seals User Guide¶
The Suite of Environments for Algorithms that Learn Specifications, or seals, is a toolkit for evaluating specification learning algorithms, such as reward or imitation learning. The environments are compatible with Gym, but are designed to test algorithms that learn from user data, without requiring a procedurally specified reward function.
There are two types of environments in seals:
Diagnostic Tasks which test individual facets of algorithm performance in isolation.
Renovated Environments, adaptations of widely-used benchmarks such as MuJoCo continuous control tasks to be suitable for specification learning benchmarks. In particular, this involves removing any side-channel sources of reward information (such as episode boundaries, the score appearing in the observation, etc) and including all the information needed to compute the reward in the observation space.
seals is under active development and we intend to add more categories of tasks soon.
User Guide
Environments
Common
Citing seals¶
To cite this project in publications:
@misc{seals,
author = {Adam Gleave and Pedro Freire and Steven Wang and Sam Toyer},
title = {{seals}: Suite of Environments for Algorithms that Learn Specifications},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/HumanCompatibleAI/seals}},
}